Spatial Analysis in R
Hannah Owens
18 May, 2020
The Plan
- Intro to GIS in R
- Importing/Exporting spatial data
- Projections, extents, and units
- Spatial data manipulation
- Intro to spatial analysis
Intro to GIS (in R)
What do you mean, GIS?
“a framework for gathering, managing, and analyzing spatial data”
GIS in R
Data Types
Vector Data
- Spreadsheets with a geometry columns
- Vertices and paths
Vector Data: Points
- X - Y coordinates in a spatial reference frame
- Generally latitude and longitude
- Careful! X = longitude, Y = latitude!
- e.g. locality
Vector Data: Lines
- Links bewteen X - Y coordinates
- Represent one-dimensional features
- e.g. rivers, streets, movement paths
Vector Data: Polygons
- Connect X - Y coordinates and close the path
- Represent two-dimensional features
- e.g. conservation areas, country boundaries, distribution limits
Vector Data: File types
- Points can be text files (e.g. csv files) OR shapefiles
- X - Y table with additional features
- Lines and polygons must be shapefiles
Vector Data: Shapefiles
- Shapefiles actually require 4 main files:
- shp - geometry of features
- dbf - a dBase file with the attribute data for each feature
- Can be opened as a spreadsheet
- Stores data associated with each feature
- prj - projection file (more on projections in a bit)
- shx - helps a GIS program find the features in the .shp file quickly
IMPORTANT: each shapefile can only store one type of geometry - point/ polygon/ line
Raster Data
- Made up of pixels
- Each pixel has a value
- Can be continuous or discrete
- Many different file types
- GeoTIFF
- Ascii
Comparing Vectors and Rasters
Data_type | Pros | Cons |
Vector | High geographical accuracy | Doesn’t work well with continuous data |
Follows topology rules which increases integrity | Processing intensive due to topology checks | |
Used for measurements of proximity | ||
Raster | Very good for representing remote sensing data | Less aesthetically pleasing |
Performs well with map algebra | Lacks attribute data flexibility | |
Data size increases with increase in resolution |
Importing/Exporting Spatial Data
Reading Vectors
The rgdal library contains a lot of really useful GIS tools. Make friends with it.
library(rgdal)
pkmng_countries <- readOGR(dsn = "Data/",
layer = "pkmng_polygons")
OGR data source with driver: ESRI Shapefile
Source: "/Users/Hannah/Dropbox/RWorkingGroup/SpatialAnalysis1/Data", layer: "pkmng_polygons"
with 246 features
It has 12 fields
Reading Vectors
head(pkmng_countries[,5:8])
An object of class "SpatialPolygonsDataFrame"
Slot "data":
NAME AREA POP2005 REGION
0 Antigua and Barbuda 44 83039 19
1 Algeria 238174 32854159 2
2 Azerbaijan 8260 8352021 142
3 Albania 2740 3153731 150
4 Armenia 2820 3017661 142
5 Angola 124670 16095214 2
Slot "polygons":
[[1]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
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Slot "area":
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Slot "hole":
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Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] -61.68667 17.02444
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[[2]]
An object of class "Polygon"
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Slot "area":
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Slot "hole":
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Slot "ringDir":
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Slot "coords":
[,1] [,2]
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Slot "labpt":
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Slot "ID":
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Slot "area":
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[[2]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 2.627813 28.172110
Slot "area":
[1] 213.9407
Slot "hole":
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Slot "ringDir":
[1] 1
Slot "coords":
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[27,] 1.169662 21.10254
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[41,] -1.668056 33.26111
[42,] -1.747222 34.74721
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[44,] 0.950000 36.45027
[45,] 2.963610 36.80222
Slot "plotOrder":
[1] 1
Slot "labpt":
[1] 2.627813 28.172110
Slot "ID":
[1] "1"
Slot "area":
[1] 213.9407
[[3]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 47.67525 40.34927
Slot "area":
[1] 8.494524
Slot "hole":
[1] FALSE
Slot "ringDir":
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Slot "coords":
[,1] [,2]
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[20,] 46.19443 41.68582
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[22,] 46.57138 41.87193
[[2]]
An object of class "Polygon"
Slot "labpt":
[1] 45.49023 39.39222
Slot "area":
[1] 0.5056316
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
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[2,] 45.81999 39.54972
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[4,] 45.00443 39.41638
[5,] 44.81304 39.63082
[6,] 44.77886 39.70638
[7,] 45.08332 39.76805
[[3]]
An object of class "Polygon"
Slot "labpt":
[1] 45.53453 40.63508
Slot "area":
[1] 0.001693513
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 45.51305 40.60722
[2,] 45.51749 40.66554
[3,] 45.57305 40.63249
[4,] 45.51305 40.60722
[[4]]
An object of class "Polygon"
Slot "labpt":
[1] 45.01879 41.05240
Slot "area":
[1] 0.0009946189
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 45.04527 41.03527
[2,] 45.00999 41.03305
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[4,] 45.04527 41.03527
[[5]]
An object of class "Polygon"
Slot "labpt":
[1] 45.22342 40.98129
Slot "area":
[1] 0.0003603638
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 45.24527 40.97694
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[4,] 45.24527 40.97694
Slot "plotOrder":
[1] 1 2 3 4 5
Slot "labpt":
[1] 47.67525 40.34927
Slot "ID":
[1] "2"
Slot "area":
[1] 9.003204
[[4]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 20.07437 41.13255
Slot "area":
[1] 2.890429
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 19.43621 41.02107
[2,] 19.60056 41.79666
[3,] 19.36777 41.84900
[4,] 19.64583 42.61805
[5,] 20.07142 42.56091
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[12,] 19.86305 40.03972
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[14,] 19.47861 40.35027
[15,] 19.43621 41.02107
Slot "plotOrder":
[1] 1
Slot "labpt":
[1] 20.07437 41.13255
Slot "ID":
[1] "3"
Slot "area":
[1] 2.890429
[[5]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 44.98386 40.30433
Slot "area":
[1] 3.208623
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 45.15387 41.19860
[2,] 46.00194 40.22555
[3,] 45.59582 39.97804
[4,] 46.54138 39.56444
[5,] 46.54038 38.87559
[6,] 46.17825 38.84115
[7,] 45.81999 39.54972
[8,] 45.08332 39.76805
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[12,] 43.75194 40.74000
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[14,] 45.02294 41.29705
[15,] 45.15387 41.19860
[[2]]
An object of class "Polygon"
Slot "labpt":
[1] 45.53453 40.63508
Slot "area":
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Slot "hole":
[1] TRUE
Slot "ringDir":
[1] -1
Slot "coords":
[,1] [,2]
[1,] 45.57305 40.63249
[2,] 45.51749 40.66554
[3,] 45.51305 40.60722
[4,] 45.57305 40.63249
[[3]]
An object of class "Polygon"
Slot "labpt":
[1] 45.01879 41.05240
Slot "area":
[1] 0.0009946189
Slot "hole":
[1] TRUE
Slot "ringDir":
[1] -1
Slot "coords":
[,1] [,2]
[1,] 45.00999 41.03305
[2,] 45.04527 41.03527
[3,] 45.00111 41.08888
[4,] 45.00999 41.03305
[[4]]
An object of class "Polygon"
Slot "labpt":
[1] 45.22342 40.98129
Slot "area":
[1] 0.0003603638
Slot "hole":
[1] TRUE
Slot "ringDir":
[1] -1
Slot "coords":
[,1] [,2]
[1,] 45.19582 40.99777
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[4,] 45.19582 40.99777
Slot "plotOrder":
[1] 1 2 3 4
Slot "labpt":
[1] 44.98386 40.30433
Slot "ID":
[1] "4"
Slot "area":
[1] 3.208623
[[6]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] 17.55246 -12.35038
Slot "area":
[1] 103.8622
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 13.99750 -5.848612
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An object of class "Polygon"
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Slot "area":
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Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
[1,] 13.08889 -4.662500
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An object of class "Polygon"
Slot "labpt":
[1] 11.73981 -16.69852
Slot "area":
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Slot "hole":
[1] FALSE
Slot "ringDir":
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Slot "coords":
[,1] [,2]
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Slot "plotOrder":
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Slot "ID":
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Slot "area":
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Slot "plotOrder":
[1] 2 6 3 5 4 1
Slot "bbox":
min max
x -61.88722 50.37499
y -18.01639 42.61805
Slot "proj4string":
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
Reading Vectors: Tidyverse
There is a tidyverse alternative
- View spatial objects
- Edit the associated data
library(spdplyr)
pkmng_countries
class : SpatialPolygonsDataFrame
features : 246
extent : -180, 180, -90, 83.57027 (xmin, xmax, ymin, ymax)
variables : 12
# A tibble: 246 x 12
FIPS ISO2 ISO3 UN NAME AREA POP2005 REGION SUBREGI LON LAT
<fct> <fct> <fct> <int> <fct> <int> <dbl> <int> <int> <dbl> <dbl>
1 AC AG ATG 28 Anti… 44 8.30e4 19 29 -61.8 17.1
2 AG DZ DZA 12 Alge… 238174 3.29e7 2 15 2.63 28.2
3 AJ AZ AZE 31 Azer… 8260 8.35e6 142 145 47.4 40.4
4 AL AL ALB 8 Alba… 2740 3.15e6 150 39 20.1 41.1
5 AM AM ARM 51 Arme… 2820 3.02e6 142 145 44.6 40.5
6 AO AO AGO 24 Ango… 124670 1.61e7 2 17 17.5 -12.3
7 AQ AS ASM 16 Amer… 20 6.41e4 9 61 -171. -14.3
8 AR AR ARG 32 Arge… 273669 3.87e7 19 5 -65.2 -35.4
9 AS AU AUS 36 Aust… 768230 2.03e7 9 53 136. -25.0
10 BA BH BHR 48 Bahr… 71 7.25e5 142 145 50.6 26.0
# … with 236 more rows, and 1 more variable: pkmn_rc <int>
Reading Vectors: Tidyverse
Use tidyverse functions to manipulate spatial data:
#filter out all countries without any Pokemon sightings, and let's rename the column while we're at it:
pkmng_countries %>%
filter(pkmn_rc > 0) %>%
rename(Country = NAME)
class : SpatialPolygonsDataFrame
features : 72
extent : -179.999, 179.999, -55.9175, 83.10942 (xmin, xmax, ymin, ymax)
variables : 12
# A tibble: 72 x 12
FIPS ISO2 ISO3 UN Country AREA POP2005 REGION SUBREGI LON LAT
<fct> <fct> <fct> <int> <fct> <int> <dbl> <int> <int> <dbl> <dbl>
1 AO AO AGO 24 Angola 124670 1.61e7 2 17 17.5 -12.3
2 AR AR ARG 32 Argent… 273669 3.87e7 19 5 -65.2 -35.4
3 AS AU AUS 36 Austra… 768230 2.03e7 9 53 136. -25.0
4 BK BA BIH 70 Bosnia… 5120 3.92e6 150 39 17.8 44.2
5 BM MM MMR 104 Burma 65755 4.80e7 142 35 96.0 21.7
6 BR BR BRA 76 Brazil 845942 1.87e8 19 5 -53.1 -10.8
7 BX BN BRN 96 Brunei… 527 3.74e5 142 35 115. 4.47
8 CA CA CAN 124 Canada 909351 3.23e7 19 21 -109. 59.1
9 CB KH KHM 116 Cambod… 17652 1.40e7 142 35 105. 12.7
10 CI CL CHL 152 Chile 74880 1.63e7 19 5 -69.4 -23.4
# … with 62 more rows, and 1 more variable: pkmn_rc <int>
Reading Vectors: Point data
pkmng_points_dat <- read.csv("Data/pkmng_points.csv")
pkmng_points_dat
pokemonId latitude longitude appearedLocalTime cellId_90m
1 16 53.685735 -0.443450 2016-09-04T09:31:44Z 5.222182e+18
2 60 46.004027 8.957153 2016-09-06T07:57:06Z 5.153294e+18
3 19 46.235403 15.254241 2016-09-04T08:33:55Z 5.144642e+18
4 16 50.675427 14.541165 2016-09-04T09:23:24Z 5.118738e+18
5 23 44.494201 11.344701 2016-09-04T16:02:16Z 5.152070e+18
6 120 50.187740 14.657816 2016-09-03T15:51:17Z 5.119451e+18
7 46 33.555176 -84.349070 2016-09-07T01:05:11Z 9.868787e+18
8 10 46.004713 8.954149 2016-09-06T06:22:14Z 5.153294e+18
9 27 33.903287 -118.083142 2016-09-06T05:13:44Z 9.278210e+18
10 23 34.274030 -119.212599 2016-09-06T14:49:44Z 9.289152e+18
11 41 50.741580 -1.965787 2016-09-06T16:12:41Z 5.220700e+18
12 16 46.033446 8.954188 2016-09-03T14:26:01Z 5.153299e+18
13 16 40.146502 -83.028375 2016-09-05T02:55:32Z 9.815863e+18
14 16 45.579949 9.285312 2016-09-05T09:23:42Z 5.154011e+18
15 16 35.448673 -97.322755 2016-09-02T22:28:00Z 9.777943e+18
16 147 45.380179 -122.763195 2016-09-05T07:22:22Z 6.094904e+18
17 16 49.967695 16.396205 2016-09-06T11:08:17Z 5.120010e+18
18 16 48.753564 2.408455 2016-09-04T12:39:46Z 5.180957e+18
19 37 41.674940 2.791325 2016-09-04T21:56:36Z 1.349697e+18
20 98 13.770166 100.573875 2016-09-03T14:13:56Z 3.522552e+18
21 133 46.007452 8.952712 2016-09-05T13:24:29Z 5.153294e+18
22 7 53.542414 -113.492265 2016-09-03T06:24:37Z 6.025854e+18
23 100 22.469160 114.001923 2016-09-06T13:25:55Z 3.748104e+18
24 13 53.684561 -0.440232 2016-09-04T19:58:56Z 5.222182e+18
25 98 50.197899 14.364426 2016-09-05T17:19:25Z 5.119398e+18
26 41 33.757346 -84.392764 2016-09-05T21:39:36Z 9.868798e+18
27 19 49.258775 14.720308 2016-09-03T15:17:44Z 5.119672e+18
28 7 43.770844 -79.212643 2016-09-08T00:51:09Z 9.931792e+18
29 10 50.601141 15.333217 2016-09-03T16:39:40Z 5.120231e+18
30 104 34.101062 -117.944351 2016-09-05T01:27:55Z 9.278216e+18
31 129 40.922980 9.517510 2016-09-07T22:21:55Z 1.358199e+18
32 19 20.525571 -97.460576 2016-09-04T14:43:12Z 9.645139e+18
33 13 46.010852 8.955257 2016-09-05T13:48:50Z 5.153294e+18
34 96 43.707624 -79.395959 2016-09-04T22:55:47Z 9.811992e+18
35 16 48.212566 16.382916 2016-09-03T19:13:50Z 5.146778e+18
36 27 25.680850 -100.403883 2016-09-06T21:43:27Z 9.683469e+18
37 114 26.883304 81.003898 2016-09-05T03:12:53Z 4.151161e+18
38 21 39.752978 -105.005474 2016-09-04T04:16:51Z 9.758307e+18
39 21 20.524649 -97.423813 2016-09-07T06:51:41Z 9.645092e+18
40 13 30.141500 -90.934857 2016-09-07T04:44:02Z 9.665089e+18
41 17 52.367802 -7.717041 2016-09-05T00:57:25Z 5.207061e+18
42 19 38.917039 -77.224034 2016-09-05T16:59:55Z 9.923201e+18
43 19 34.900966 -77.530368 2016-09-04T20:28:26Z 9.919483e+18
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431 5.222800e+18 evening 19 44
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434 5.152479e+18 evening 16 48
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446 9.683468e+18 night 4 37
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451 9.786149e+18 night 3 0
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461 5.119671e+18 evening 19 1
462 9.868788e+18 night 1 20
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467 5.074760e+18 evening 19 42
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470 1.343377e+18 night 1 28
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483 6.025883e+18 evening 17 6
appearedDayOfWeek appearedDay appearedMonth appearedYear terrainType
1 Thursday 4 8 2016 12
2 Saturday 6 8 2016 5
3 Thursday 4 8 2016 5
4 Thursday 4 8 2016 14
5 Thursday 4 8 2016 13
6 Wednesday 3 8 2016 12
7 Sunday 7 8 2016 13
8 Saturday 6 8 2016 5
9 Saturday 6 8 2016 13
10 Saturday 6 8 2016 13
11 Saturday 6 8 2016 13
12 Wednesday 3 8 2016 5
13 Friday 5 8 2016 13
14 Friday 5 8 2016 13
15 Wednesday 3 8 2016 10
16 Friday 5 8 2016 13
17 Saturday 6 8 2016 5
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19 Thursday 4 8 2016 8
20 Wednesday 3 8 2016 13
21 Friday 5 8 2016 5
22 Wednesday 3 8 2016 13
23 Saturday 6 8 2016 12
24 Thursday 4 8 2016 12
25 Friday 5 8 2016 12
26 Friday 5 8 2016 13
27 Wednesday 3 8 2016 14
28 dummy_day 8 8 2016 13
29 Wednesday 3 8 2016 14
30 Friday 5 8 2016 13
31 dummy_day 8 8 2016 8
32 Thursday 4 8 2016 14
33 Friday 5 8 2016 5
34 Friday 5 8 2016 13
35 Wednesday 3 8 2016 13
36 Saturday 6 8 2016 13
37 Friday 5 8 2016 12
38 Thursday 4 8 2016 13
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40 Sunday 7 8 2016 14
41 Friday 5 8 2016 10
42 Friday 5 8 2016 5
43 Thursday 4 8 2016 14
44 Friday 5 8 2016 0
45 Friday 5 8 2016 13
46 Friday 5 8 2016 12
47 Saturday 6 8 2016 14
48 Sunday 7 8 2016 13
49 Sunday 7 8 2016 13
50 Friday 5 8 2016 14
51 Wednesday 3 8 2016 13
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53 Wednesday 3 8 2016 14
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55 Thursday 4 8 2016 10
56 Sunday 7 8 2016 13
57 Friday 5 8 2016 13
58 Saturday 6 8 2016 7
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60 Saturday 6 8 2016 13
61 Thursday 4 8 2016 13
62 Saturday 6 8 2016 13
63 Saturday 6 8 2016 5
64 Wednesday 3 8 2016 0
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66 Wednesday 3 8 2016 8
67 Wednesday 3 8 2016 12
68 Sunday 7 8 2016 14
69 Friday 5 8 2016 5
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71 Thursday 4 8 2016 7
72 dummy_day 8 8 2016 13
73 Sunday 7 8 2016 14
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76 Thursday 4 8 2016 12
77 Thursday 4 8 2016 13
78 Saturday 6 8 2016 13
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80 Wednesday 3 8 2016 14
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82 Wednesday 3 8 2016 5
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88 Wednesday 3 8 2016 14
89 Friday 5 8 2016 8
90 Wednesday 3 8 2016 12
91 Sunday 7 8 2016 12
92 Friday 5 8 2016 12
93 Friday 5 8 2016 14
94 Sunday 7 8 2016 13
95 Wednesday 3 8 2016 13
96 Saturday 6 8 2016 5
97 Sunday 7 8 2016 13
98 Saturday 6 8 2016 13
99 Saturday 6 8 2016 13
100 Sunday 7 8 2016 13
101 Thursday 4 8 2016 13
102 Sunday 7 8 2016 16
103 Saturday 6 8 2016 13
104 Thursday 4 8 2016 13
105 Sunday 7 8 2016 14
106 Friday 5 8 2016 13
107 Wednesday 3 8 2016 13
108 Thursday 4 8 2016 4
109 Saturday 6 8 2016 13
110 Saturday 6 8 2016 0
111 dummy_day 8 8 2016 14
112 Saturday 6 8 2016 5
113 Wednesday 3 8 2016 5
114 Friday 5 8 2016 12
115 Saturday 6 8 2016 13
116 Friday 5 8 2016 13
117 Sunday 7 8 2016 0
118 Sunday 7 8 2016 7
119 Friday 5 8 2016 13
120 Thursday 4 8 2016 12
121 Sunday 7 8 2016 13
122 Saturday 6 8 2016 10
123 Saturday 6 8 2016 12
124 Thursday 4 8 2016 13
125 Saturday 6 8 2016 13
126 Wednesday 3 8 2016 13
127 Thursday 4 8 2016 13
128 Saturday 6 8 2016 13
129 Thursday 4 8 2016 12
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131 Friday 5 8 2016 13
132 Wednesday 3 8 2016 10
133 Friday 5 8 2016 10
134 Friday 5 8 2016 13
135 Saturday 6 8 2016 13
136 Friday 5 8 2016 14
137 Saturday 6 8 2016 12
138 Saturday 6 8 2016 8
139 Thursday 4 8 2016 13
140 Friday 5 8 2016 0
141 Sunday 7 8 2016 13
142 Wednesday 3 8 2016 13
143 Friday 5 8 2016 14
144 Wednesday 3 8 2016 13
145 dummy_day 8 8 2016 14
146 Wednesday 3 8 2016 5
147 Wednesday 3 8 2016 13
148 Sunday 7 8 2016 13
149 Wednesday 3 8 2016 13
150 Sunday 7 8 2016 8
151 Wednesday 3 8 2016 13
152 Wednesday 3 8 2016 13
153 Sunday 7 8 2016 13
154 Friday 5 8 2016 13
155 Saturday 6 8 2016 5
156 Thursday 4 8 2016 14
157 Thursday 4 8 2016 14
158 Thursday 4 8 2016 12
159 Wednesday 3 8 2016 14
160 Friday 5 8 2016 5
161 Wednesday 3 8 2016 12
162 Friday 5 8 2016 14
163 Sunday 7 8 2016 13
164 Friday 5 8 2016 13
165 Thursday 4 8 2016 13
166 Wednesday 3 8 2016 13
167 Friday 5 8 2016 12
168 Saturday 6 8 2016 13
169 Saturday 6 8 2016 13
170 Friday 5 8 2016 13
171 Saturday 6 8 2016 13
172 dummy_day 8 8 2016 14
173 Thursday 4 8 2016 12
174 Friday 5 8 2016 5
175 Thursday 4 8 2016 12
176 Saturday 6 8 2016 13
177 Wednesday 3 8 2016 0
178 Thursday 4 8 2016 13
179 Sunday 7 8 2016 7
180 Wednesday 3 8 2016 13
181 Saturday 6 8 2016 5
182 Saturday 6 8 2016 13
183 Saturday 6 8 2016 13
184 Sunday 7 8 2016 8
185 Wednesday 3 8 2016 0
186 Saturday 6 8 2016 13
187 dummy_day 8 8 2016 13
188 Thursday 4 8 2016 10
189 Friday 5 8 2016 0
190 Friday 5 8 2016 5
191 Saturday 6 8 2016 12
192 Saturday 6 8 2016 0
193 Saturday 6 8 2016 13
194 Friday 5 8 2016 13
195 Wednesday 3 8 2016 0
196 Saturday 6 8 2016 5
197 Wednesday 3 8 2016 12
198 Wednesday 3 8 2016 12
199 Saturday 6 8 2016 5
200 Wednesday 3 8 2016 5
201 Saturday 6 8 2016 5
202 Friday 5 8 2016 13
203 Saturday 6 8 2016 14
204 Saturday 6 8 2016 13
205 Saturday 6 8 2016 13
206 Thursday 4 8 2016 12
207 Sunday 7 8 2016 5
208 Sunday 7 8 2016 13
209 Thursday 4 8 2016 13
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211 Friday 5 8 2016 10
212 Friday 5 8 2016 13
213 Sunday 7 8 2016 13
214 Friday 5 8 2016 13
215 Saturday 6 8 2016 12
216 Saturday 6 8 2016 7
217 Thursday 4 8 2016 13
218 Sunday 7 8 2016 5
219 Wednesday 3 8 2016 10
220 Saturday 6 8 2016 0
221 Friday 5 8 2016 10
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223 Saturday 6 8 2016 12
224 Saturday 6 8 2016 5
225 Saturday 6 8 2016 13
226 Friday 5 8 2016 13
227 Saturday 6 8 2016 8
228 Wednesday 3 8 2016 13
229 Friday 5 8 2016 14
230 Saturday 6 8 2016 5
231 Wednesday 3 8 2016 13
232 Friday 5 8 2016 12
233 Thursday 4 8 2016 4
234 Thursday 4 8 2016 13
235 Saturday 6 8 2016 8
236 Thursday 4 8 2016 13
237 Thursday 4 8 2016 5
238 Wednesday 3 8 2016 14
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240 Saturday 6 8 2016 5
241 Saturday 6 8 2016 14
242 Friday 5 8 2016 13
243 Saturday 6 8 2016 13
244 Friday 5 8 2016 0
245 Wednesday 3 8 2016 5
246 Thursday 4 8 2016 12
247 Wednesday 3 8 2016 13
248 Wednesday 3 8 2016 0
249 Wednesday 3 8 2016 12
250 Friday 5 8 2016 1
251 Friday 5 8 2016 12
252 Sunday 7 8 2016 13
253 Saturday 6 8 2016 13
254 Saturday 6 8 2016 13
255 Thursday 4 8 2016 10
256 Saturday 6 8 2016 13
257 Saturday 6 8 2016 13
258 Friday 5 8 2016 13
259 Wednesday 3 8 2016 14
260 Thursday 4 8 2016 12
261 Thursday 4 8 2016 10
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263 Thursday 4 8 2016 4
264 Thursday 4 8 2016 13
265 Thursday 4 8 2016 13
266 Saturday 6 8 2016 13
267 Thursday 4 8 2016 5
268 Saturday 6 8 2016 13
269 Friday 5 8 2016 9
270 Thursday 4 8 2016 0
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273 Friday 5 8 2016 12
274 Sunday 7 8 2016 13
275 Friday 5 8 2016 13
276 dummy_day 8 8 2016 14
277 Friday 5 8 2016 13
278 Sunday 7 8 2016 13
279 Saturday 6 8 2016 13
280 Sunday 7 8 2016 13
281 Thursday 4 8 2016 5
282 Saturday 6 8 2016 5
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284 Saturday 6 8 2016 14
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288 Wednesday 3 8 2016 13
289 Saturday 6 8 2016 12
290 Thursday 4 8 2016 8
291 Saturday 6 8 2016 13
292 Thursday 4 8 2016 13
293 Saturday 6 8 2016 0
294 Friday 5 8 2016 12
295 Friday 5 8 2016 13
296 Thursday 4 8 2016 13
297 Saturday 6 8 2016 5
298 Saturday 6 8 2016 0
299 Friday 5 8 2016 13
300 Saturday 6 8 2016 5
301 Friday 5 8 2016 14
302 Friday 5 8 2016 13
303 Wednesday 3 8 2016 13
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305 Thursday 4 8 2016 12
306 Thursday 4 8 2016 14
307 Friday 5 8 2016 12
308 Friday 5 8 2016 13
309 Saturday 6 8 2016 5
310 Saturday 6 8 2016 12
311 Sunday 7 8 2016 14
312 Thursday 4 8 2016 13
313 Saturday 6 8 2016 13
314 Saturday 6 8 2016 0
315 Saturday 6 8 2016 13
316 Friday 5 8 2016 14
317 dummy_day 8 8 2016 1
318 Sunday 7 8 2016 13
319 Wednesday 3 8 2016 13
320 Saturday 6 8 2016 12
321 Thursday 4 8 2016 14
322 Sunday 7 8 2016 13
323 Saturday 6 8 2016 13
324 Sunday 7 8 2016 13
325 Sunday 7 8 2016 13
326 Thursday 4 8 2016 0
327 Friday 5 8 2016 14
328 Wednesday 3 8 2016 5
329 Wednesday 3 8 2016 13
330 Thursday 4 8 2016 12
331 Wednesday 3 8 2016 0
332 Friday 5 8 2016 1
333 Friday 5 8 2016 13
334 Saturday 6 8 2016 8
335 Sunday 7 8 2016 13
336 dummy_day 8 8 2016 8
337 Friday 5 8 2016 8
338 Saturday 6 8 2016 13
339 Wednesday 3 8 2016 13
340 Saturday 6 8 2016 5
341 Sunday 7 8 2016 12
342 Thursday 4 8 2016 14
343 Saturday 6 8 2016 0
344 Wednesday 3 8 2016 14
345 Sunday 7 8 2016 13
346 Sunday 7 8 2016 14
347 Wednesday 3 8 2016 12
348 Thursday 4 8 2016 14
349 Saturday 6 8 2016 12
350 Friday 5 8 2016 14
351 Thursday 4 8 2016 0
352 Friday 5 8 2016 13
353 Thursday 4 8 2016 12
354 Wednesday 3 8 2016 10
355 Wednesday 3 8 2016 5
356 Friday 5 8 2016 8
357 Wednesday 3 8 2016 13
358 Sunday 7 8 2016 14
359 Saturday 6 8 2016 14
360 Friday 5 8 2016 5
361 Saturday 6 8 2016 13
362 Sunday 7 8 2016 13
363 Friday 5 8 2016 13
364 Friday 5 8 2016 13
365 Friday 5 8 2016 14
366 Wednesday 3 8 2016 13
367 Wednesday 3 8 2016 10
368 Sunday 7 8 2016 12
369 Thursday 4 8 2016 13
370 Wednesday 3 8 2016 13
371 Sunday 7 8 2016 8
372 Wednesday 3 8 2016 13
373 Saturday 6 8 2016 14
374 Saturday 6 8 2016 5
375 Saturday 6 8 2016 8
376 Wednesday 3 8 2016 12
377 Saturday 6 8 2016 10
378 Friday 5 8 2016 13
379 Saturday 6 8 2016 8
380 Friday 5 8 2016 14
381 Friday 5 8 2016 13
382 Sunday 7 8 2016 14
383 Saturday 6 8 2016 14
384 Thursday 4 8 2016 5
385 Saturday 6 8 2016 5
386 Sunday 7 8 2016 12
387 dummy_day 8 8 2016 13
388 Saturday 6 8 2016 10
389 Friday 5 8 2016 10
390 Wednesday 3 8 2016 13
391 Sunday 7 8 2016 0
392 Friday 5 8 2016 13
393 Wednesday 3 8 2016 0
394 Sunday 7 8 2016 12
395 Wednesday 3 8 2016 14
396 Sunday 7 8 2016 13
397 Friday 5 8 2016 5
398 Wednesday 3 8 2016 13
399 Wednesday 3 8 2016 13
400 Friday 5 8 2016 12
401 Friday 5 8 2016 5
402 Thursday 4 8 2016 0
403 Saturday 6 8 2016 12
404 Thursday 4 8 2016 14
405 Sunday 7 8 2016 4
406 Friday 5 8 2016 13
407 Friday 5 8 2016 12
408 Friday 5 8 2016 13
409 Sunday 7 8 2016 13
410 Sunday 7 8 2016 12
411 Saturday 6 8 2016 14
412 Friday 5 8 2016 12
413 Friday 5 8 2016 1
414 Saturday 6 8 2016 5
415 Thursday 4 8 2016 4
416 Thursday 4 8 2016 0
417 Saturday 6 8 2016 7
418 Saturday 6 8 2016 4
419 Friday 5 8 2016 7
420 Friday 5 8 2016 13
421 Wednesday 3 8 2016 13
422 Friday 5 8 2016 7
423 Sunday 7 8 2016 13
424 Thursday 4 8 2016 14
425 Friday 5 8 2016 12
426 Friday 5 8 2016 10
427 Friday 5 8 2016 13
428 Saturday 6 8 2016 13
429 Friday 5 8 2016 5
430 Saturday 6 8 2016 13
431 Wednesday 3 8 2016 13
432 Wednesday 3 8 2016 5
433 Thursday 4 8 2016 14
434 Saturday 6 8 2016 12
435 Thursday 4 8 2016 5
436 Wednesday 3 8 2016 0
437 Wednesday 3 8 2016 14
438 Wednesday 3 8 2016 12
439 Friday 5 8 2016 10
440 Thursday 4 8 2016 14
441 Saturday 6 8 2016 14
442 Sunday 7 8 2016 13
443 Sunday 7 8 2016 0
444 Wednesday 3 8 2016 12
445 Saturday 6 8 2016 0
446 dummy_day 8 8 2016 13
447 Saturday 6 8 2016 13
448 Friday 5 8 2016 13
449 Wednesday 3 8 2016 14
450 Sunday 7 8 2016 14
451 Sunday 7 8 2016 13
452 Saturday 6 8 2016 13
453 Friday 5 8 2016 13
454 Friday 5 8 2016 13
455 Sunday 7 8 2016 14
456 Saturday 6 8 2016 13
457 Wednesday 3 8 2016 13
458 Thursday 4 8 2016 8
459 Friday 5 8 2016 5
460 Friday 5 8 2016 12
461 Saturday 6 8 2016 14
462 Saturday 6 8 2016 13
463 Thursday 4 8 2016 0
464 Sunday 7 8 2016 12
465 Sunday 7 8 2016 13
466 Saturday 6 8 2016 14
467 Sunday 7 8 2016 7
468 Thursday 4 8 2016 5
469 Wednesday 3 8 2016 5
470 Saturday 6 8 2016 0
471 Friday 5 8 2016 0
472 Friday 5 8 2016 14
473 Thursday 4 8 2016 0
474 Saturday 6 8 2016 14
475 Wednesday 3 8 2016 14
476 Saturday 6 8 2016 4
477 Wednesday 3 8 2016 13
478 Thursday 4 8 2016 7
479 Wednesday 3 8 2016 0
480 dummy_day 8 8 2016 13
481 Wednesday 3 8 2016 7
482 Thursday 4 8 2016 13
483 Thursday 4 8 2016 12
closeToWater city continent weather
1 FALSE London Europe Clear
2 FALSE Zurich Europe Clear
3 FALSE Ljubljana Europe MostlyCloudy
4 FALSE Prague Europe MostlyCloudy
5 FALSE Rome Europe MostlyCloudy
6 FALSE Prague Europe MostlyCloudy
7 FALSE New_York America Clear
8 FALSE Zurich Europe Clear
9 FALSE Los_Angeles America Clear
10 TRUE Los_Angeles America Clear
11 TRUE London Europe MostlyCloudy
12 FALSE Zurich Europe Clear
13 FALSE New_York America Clear
14 FALSE Rome Europe MostlyCloudy
15 FALSE Chicago America Clear
16 FALSE Los_Angeles America Clear
17 FALSE Prague Europe PartlyCloudy
18 FALSE Paris Europe LightRain
19 TRUE Madrid Europe PartlyCloudy
20 FALSE Bangkok Asia Clear
21 FALSE Zurich Europe Clear
22 FALSE Edmonton America Clear
23 TRUE Hong_Kong Asia Clear
24 FALSE London Europe Clear
25 FALSE Prague Europe MostlyCloudy
26 FALSE New_York America Clear
27 FALSE Prague Europe MostlyCloudy
28 TRUE Toronto America LightRain
29 FALSE Prague Europe MostlyCloudy
30 FALSE Los_Angeles America Clear
31 TRUE Rome Europe Overcast
32 FALSE Mexico_City America Humid
33 FALSE Zurich Europe Clear
34 TRUE Toronto America Clear
35 FALSE Vienna Europe MostlyCloudy
36 FALSE Monterrey America MostlyCloudy
37 FALSE Kolkata Asia Clear
38 FALSE Denver America PartlyCloudy
39 FALSE Mexico_City America PartlyCloudy
40 FALSE Chicago America Clear
41 FALSE Dublin Europe Overcast
42 FALSE New_York America Clear
43 FALSE New_York America Clear
44 TRUE Chicago America Clear
45 FALSE Prague Europe PartlyCloudy
46 FALSE Edmonton America Clear
47 FALSE Prague Europe Clear
48 FALSE Los_Angeles America Clear
49 TRUE Chicago America PartlyCloudy
50 FALSE New_York America Clear
51 FALSE Mexico_City America HumidandOvercast
52 FALSE Paris Europe PartlyCloudy
53 FALSE Chicago America Clear
54 TRUE Rome Europe Clear
55 FALSE London Europe Clear
56 FALSE New_York America Clear
57 FALSE Warsaw Europe Clear
58 FALSE Los_Angeles America Clear
59 FALSE New_York America Clear
60 FALSE Vienna Europe MostlyCloudy
61 FALSE Bangkok Asia Clear
62 FALSE Kolkata Asia Clear
63 FALSE Ljubljana Europe MostlyCloudy
64 TRUE Oslo Europe Clear
65 FALSE London Europe Clear
66 FALSE New_York America Drizzle
67 FALSE Melbourne Australia Overcast
68 FALSE New_York America Clear
69 TRUE Winnipeg America Clear
70 FALSE Edmonton America PartlyCloudy
71 FALSE Phoenix America Clear
72 FALSE New_York America Clear
73 FALSE Rome Europe Clear
74 FALSE Rome Europe Clear
75 FALSE New_York America Clear
76 FALSE Prague Europe PartlyCloudy
77 FALSE Vienna Europe MostlyCloudy
78 FALSE Prague Europe PartlyCloudy
79 TRUE Brunei Asia Clear
80 FALSE Prague Europe MostlyCloudy
81 FALSE New_York America Clear
82 TRUE Stockholm Europe PartlyCloudy
83 TRUE New_York America Clear
84 TRUE Vancouver America Drizzle
85 FALSE Ljubljana Europe PartlyCloudy
86 FALSE New_York America Clear
87 TRUE Regina America Rain
88 FALSE New_York America Clear
89 TRUE Madrid Europe MostlyCloudy
90 FALSE Vancouver America Clear
91 FALSE Bucharest Europe Clear
92 FALSE Rome Europe Clear
93 FALSE Mexico_City America MostlyCloudy
94 FALSE Los_Angeles America Clear
95 FALSE London Europe Clear
96 FALSE Stockholm Europe Clear
97 FALSE Los_Angeles America Clear
98 FALSE Prague Europe PartlyCloudy
99 FALSE Melbourne Australia Overcast
100 FALSE Edmonton America PartlyCloudy
101 TRUE Toronto America Clear
102 TRUE Dubai Asia Clear
103 FALSE Edmonton America Clear
104 TRUE Puerto_Rico America Clear
105 FALSE New_York America Clear
106 FALSE Melbourne Australia Overcast
107 TRUE Vancouver America PartlyCloudy
108 FALSE New_York America Clear
109 FALSE Chicago America Clear
110 TRUE Hong_Kong Asia MostlyCloudy
111 FALSE Chicago America Clear
112 FALSE Rome Europe Clear
113 FALSE Stockholm Europe MostlyCloudy
114 FALSE Prague Europe MostlyCloudy
115 FALSE Prague Europe PartlyCloudy
116 TRUE New_York America Clear
117 TRUE Bahia America PartlyCloudy
118 FALSE Phoenix America Clear
119 FALSE Los_Angeles America Clear
120 TRUE London Europe Clear
121 FALSE Edmonton America PartlyCloudy
122 TRUE Auckland Pacific Clear
123 FALSE Prague Europe Clear
124 TRUE Denver America Clear
125 FALSE Amsterdam Europe MostlyCloudy
126 TRUE Denver America PartlyCloudy
127 FALSE Denver America Clear
128 FALSE Rome Europe Clear
129 FALSE Prague Europe MostlyCloudy
130 TRUE Rome Europe PartlyCloudy
131 TRUE Sao_Paulo America PartlyCloudy
132 FALSE Dublin Europe PartlyCloudy
133 FALSE Denver America Clear
134 FALSE London Europe Clear
135 FALSE New_York America Clear
136 FALSE Mexico_City America Clear
137 FALSE Chicago America Clear
138 FALSE New_York America Clear
139 TRUE New_York America Clear
140 TRUE New_York America LightRain
141 FALSE Ljubljana Europe PartlyCloudy
142 FALSE Kuala_Lumpur Asia Clear
143 FALSE Prague Europe Overcast
144 FALSE Warsaw Europe MostlyCloudy
145 FALSE New_York America Clear
146 FALSE Stockholm Europe MostlyCloudy
147 TRUE London Europe Breezy
148 FALSE Chicago America PartlyCloudy
149 FALSE Paris Europe Clear
150 FALSE New_York America PartlyCloudy
151 TRUE Helsinki Europe MostlyCloudy
152 FALSE Madrid Europe PartlyCloudy
153 FALSE New_York America Clear
154 FALSE Paris Europe Overcast
155 FALSE London Europe Overcast
156 FALSE Mexico_City America Clear
157 FALSE Prague Europe MostlyCloudy
158 FALSE Rome Europe Clear
159 FALSE Mexico_City America Humid
160 FALSE Prague Europe Drizzle
161 FALSE Prague Europe MostlyCloudy
162 TRUE Paris Europe Clear
163 FALSE Monterrey America Clear
164 FALSE New_York America Clear
165 FALSE Rome Europe Clear
166 FALSE Madrid Europe PartlyCloudy
167 FALSE Ljubljana Europe MostlyCloudy
168 FALSE Edmonton America MostlyCloudy
169 FALSE Ljubljana Europe MostlyCloudy
170 FALSE Los_Angeles America Clear
171 FALSE Melbourne Australia Overcast
172 FALSE New_York America Clear
173 FALSE London Europe PartlyCloudy
174 FALSE New_York America Clear
175 TRUE Rome Europe PartlyCloudy
176 TRUE Chicago America Clear
177 TRUE Oslo Europe Drizzle
178 FALSE Madrid Europe PartlyCloudy
179 FALSE Phoenix America Clear
180 FALSE London Europe Clear
181 FALSE Zurich Europe Dry
182 FALSE Edmonton America Clear
183 FALSE New_York America Clear
184 FALSE New_York America Clear
185 TRUE Chicago America PartlyCloudy
186 TRUE Los_Angeles America PartlyCloudy
187 FALSE New_York America Clear
188 FALSE London Europe Clear
189 TRUE Warsaw Europe PartlyCloudy
190 FALSE New_York America Clear
191 FALSE Copenhagen Europe Clear
192 TRUE New_York America PartlyCloudy
193 FALSE London Europe Overcast
194 FALSE Prague Europe MostlyCloudy
195 TRUE Oslo Europe Clear
196 FALSE Prague Europe PartlyCloudy
197 FALSE Paris Europe MostlyCloudy
198 FALSE Prague Europe MostlyCloudy
199 FALSE Rome Europe Clear
200 FALSE Vancouver America Clear
201 FALSE Oslo Europe MostlyCloudy
202 TRUE New_York America Clear
203 FALSE Mexico_City America Clear
204 FALSE London Europe Overcast
205 TRUE Chicago America Clear
206 FALSE London Europe Clear
207 FALSE Los_Angeles America PartlyCloudy
208 TRUE Chicago America PartlyCloudy
209 TRUE Zurich Europe Overcast
210 FALSE Rome Europe Clear
211 FALSE Denver America Clear
212 FALSE Melbourne Australia Clear
213 FALSE Chicago America PartlyCloudy
214 FALSE Warsaw Europe MostlyCloudy
215 FALSE London Europe PartlyCloudy
216 FALSE Los_Angeles America PartlyCloudy
217 TRUE Stockholm Europe Overcast
218 FALSE Paris Europe Clear
219 FALSE Dublin Europe MostlyCloudy
220 TRUE New_York America Clear
221 FALSE London Europe Clear
222 FALSE Edmonton America PartlyCloudy
223 FALSE Mexico_City America PartlyCloudy
224 FALSE Prague Europe PartlyCloudy
225 FALSE New_York America Clear
226 FALSE Prague Europe MostlyCloudy
227 FALSE New_York America Clear
228 FALSE Edmonton America Clear
229 FALSE Prague Europe Overcast
230 FALSE Stockholm Europe MostlyCloudy
231 FALSE London Europe Clear
232 FALSE London Europe PartlyCloudy
233 FALSE New_York America Clear
234 FALSE London Europe Clear
235 FALSE New_York America Clear
236 FALSE Chicago America Clear
237 FALSE Prague Europe PartlyCloudy
238 FALSE New_York America Clear
239 FALSE New_York America Clear
240 FALSE Stockholm Europe Clear
241 FALSE Prague Europe MostlyCloudy
242 FALSE New_York America Clear
243 FALSE Denver America LightRain
244 TRUE Chicago America Clear
245 FALSE Prague Europe MostlyCloudy
246 FALSE Prague Europe PartlyCloudy
247 FALSE Prague Europe PartlyCloudy
248 TRUE Chicago America Clear
249 TRUE Rome Europe PartlyCloudy
250 TRUE Los_Angeles America PartlyCloudy
251 FALSE Prague Europe PartlyCloudy
252 FALSE Ljubljana Europe MostlyCloudy
253 FALSE Mexico_City America Clear
254 FALSE London Europe Overcast
255 FALSE Dublin Europe MostlyCloudy
256 FALSE Rome Europe PartlyCloudy
257 FALSE Stockholm Europe MostlyCloudy
258 FALSE Denver America Clear
259 FALSE Prague Europe MostlyCloudy
260 FALSE Prague Europe MostlyCloudy
261 FALSE Dublin Europe PartlyCloudy
262 FALSE Kuching Asia Clear
263 FALSE New_York America Clear
264 FALSE New_York America Clear
265 TRUE Los_Angeles America Clear
266 FALSE Bangkok Asia Overcast
267 TRUE Stockholm Europe PartlyCloudy
268 FALSE Warsaw Europe PartlyCloudy
269 FALSE Oslo Europe Clear
270 TRUE Los_Angeles America Clear
271 FALSE New_York America Clear
272 FALSE Rome Europe MostlyCloudy
273 FALSE Prague Europe Drizzle
274 FALSE Monterrey America MostlyCloudy
275 TRUE Buenos_Aires America/Argentina Overcast
276 FALSE New_York America Clear
277 FALSE Monterrey America PartlyCloudy
278 TRUE Warsaw Europe Clear
279 FALSE Rome Europe MostlyCloudy
280 FALSE Buenos_Aires America/Argentina Clear
281 FALSE Oslo Europe Clear
282 FALSE New_York America Clear
283 FALSE Los_Angeles America Clear
284 FALSE New_York America Clear
285 TRUE Chicago America Clear
286 FALSE Edmonton America Clear
287 TRUE Chicago America Clear
288 FALSE Los_Angeles America Clear
289 TRUE London Europe Overcast
290 TRUE Madrid Europe Clear
291 TRUE Chicago America Clear
292 FALSE Chicago America Clear
293 TRUE Hong_Kong Asia MostlyCloudy
294 FALSE Rome Europe PartlyCloudy
295 FALSE Los_Angeles America Clear
296 FALSE New_York America Clear
297 FALSE Prague Europe MostlyCloudy
298 TRUE New_York America Clear
299 FALSE Prague Europe MostlyCloudy
300 FALSE Zurich Europe Clear
301 FALSE Prague Europe Drizzle
302 FALSE Edmonton America Clear
303 FALSE Chicago America PartlyCloudy
304 FALSE Dublin Europe Overcast
305 TRUE Paris Europe Drizzle
306 FALSE Mexico_City America Overcast
307 FALSE London Europe PartlyCloudy
308 TRUE New_York America Clear
309 FALSE Rome Europe PartlyCloudy
310 FALSE Los_Angeles America Clear
311 FALSE Paris Europe Clear
312 FALSE London Europe Clear
313 TRUE Chicago America BreezyandPartlyCloudy
314 TRUE Oslo Europe Clear
315 FALSE New_York America Clear
316 FALSE New_York America Clear
317 TRUE Vancouver America Clear
318 TRUE Chicago America PartlyCloudy
319 FALSE Brunei Asia MostlyCloudy
320 FALSE Montreal America Clear
321 FALSE Amsterdam Europe Overcast
322 TRUE Buenos_Aires America/Argentina Clear
323 FALSE Ljubljana Europe Overcast
324 TRUE New_York America Rain
325 FALSE Los_Angeles America PartlyCloudy
326 TRUE Oslo Europe Clear
327 FALSE New_York America Clear
328 TRUE Stockholm Europe PartlyCloudy
329 FALSE Los_Angeles America Clear
330 FALSE London Europe Clear
331 TRUE New_York America PartlyCloudy
332 FALSE Denver America Clear
333 FALSE London Europe PartlyCloudy
334 FALSE New_York America Clear
335 FALSE Warsaw Europe Clear
336 FALSE New_York America Clear
337 FALSE New_York America Clear
338 TRUE New_York America Clear
339 FALSE Bangkok Asia MostlyCloudy
340 FALSE Rome Europe Clear
341 FALSE London Europe Clear
342 FALSE Prague Europe Overcast
343 TRUE Puerto_Rico America Clear
344 FALSE Mexico_City America HumidandOvercast
345 FALSE New_York America PartlyCloudy
346 FALSE Mexico_City America Clear
347 FALSE Prague Europe MostlyCloudy
348 FALSE New_York America PartlyCloudy
349 FALSE Rome Europe Clear
350 FALSE New_York America Clear
351 TRUE Oslo Europe MostlyCloudy
352 TRUE New_York America Clear
353 TRUE Hong_Kong Asia PartlyCloudy
354 FALSE Auckland Pacific Clear
355 FALSE Stockholm Europe MostlyCloudy
356 FALSE New_York America Clear
357 FALSE New_York America Clear
358 FALSE Prague Europe PartlyCloudy
359 FALSE Stockholm Europe Clear
360 FALSE New_York America Clear
361 TRUE New_York America PartlyCloudy
362 FALSE Edmonton America Clear
363 FALSE London Europe Clear
364 FALSE Prague Europe MostlyCloudy
365 FALSE Bangkok Asia Overcast
366 FALSE Prague Europe PartlyCloudy
367 FALSE Auckland Pacific Clear
368 FALSE London Europe Clear
369 FALSE London Europe Clear
370 FALSE Chicago America Clear
371 FALSE New_York America Clear
372 FALSE Singapore Asia Clear
373 FALSE New_York America Clear
374 FALSE Vancouver America Clear
375 FALSE New_York America Clear
376 FALSE London Europe Clear
377 FALSE Dublin Europe Overcast
378 FALSE New_York America Clear
379 TRUE Rome Europe PartlyCloudy
380 FALSE New_York America Clear
381 FALSE New_York America Clear
382 FALSE New_York America PartlyCloudy
383 FALSE Prague Europe Clear
384 FALSE Vancouver America Clear
385 FALSE Prague Europe PartlyCloudy
386 FALSE Berlin Europe Clear
387 TRUE Toronto America LightRain
388 FALSE Denver America Clear
389 FALSE New_York America Clear
390 FALSE Los_Angeles America Clear
391 TRUE Noumea Pacific MostlyCloudy
392 FALSE Los_Angeles America Clear
393 TRUE Tokyo Asia Clear
394 FALSE Chicago America Clear
395 FALSE New_York America Clear
396 TRUE Chicago America Clear
397 FALSE New_York America Clear
398 FALSE New_York America Clear
399 FALSE Rome Europe PartlyCloudy
400 TRUE London Europe Clear
401 FALSE Tokyo Asia Clear
402 TRUE London Europe Clear
403 FALSE Rome Europe Clear
404 FALSE Warsaw Europe PartlyCloudy
405 FALSE New_York America LightRain
406 TRUE Stockholm Europe Clear
407 FALSE Prague Europe MostlyCloudy
408 FALSE Denver America Clear
409 TRUE Chicago America PartlyCloudy
410 FALSE Rome Europe PartlyCloudy
411 FALSE Ho_Chi_Minh Asia Overcast
412 FALSE Chicago America Clear
413 FALSE Denver America Clear
414 TRUE Stockholm Europe Clear
415 FALSE New_York America Clear
416 TRUE Los_Angeles America Clear
417 FALSE Oslo Europe Clear
418 FALSE New_York America Clear
419 FALSE Los_Angeles America Clear
420 FALSE New_York America Clear
421 FALSE New_York America PartlyCloudy
422 FALSE Los_Angeles America Clear
423 FALSE Mexico_City America LightRain
424 FALSE Prague Europe MostlyCloudy
425 FALSE Rome Europe MostlyCloudy
426 FALSE Denver America Clear
427 FALSE Paris Europe Clear
428 TRUE Stockholm Europe Clear
429 FALSE New_York America Clear
430 TRUE New_York America LightRainandBreezy
431 FALSE London Europe Clear
432 FALSE Oslo Europe MostlyCloudy
433 FALSE Prague Europe PartlyCloudy
434 FALSE Rome Europe MostlyCloudy
435 FALSE Vancouver America Clear
436 TRUE Brunei Asia Clear
437 FALSE New_York America Clear
438 FALSE London Europe Clear
439 FALSE London Europe Clear
440 FALSE Bangkok Asia MostlyCloudy
441 FALSE Mexico_City America Clear
442 FALSE Edmonton America Clear
443 TRUE Chicago America Clear
444 FALSE Edmonton America Clear
445 TRUE Chicago America Clear
446 FALSE Monterrey America Clear
447 FALSE Prague Europe Rain
448 FALSE Ljubljana Europe MostlyCloudy
449 FALSE Mexico_City America Clear
450 FALSE Warsaw Europe PartlyCloudy
451 FALSE Chicago America Clear
452 FALSE New_York America Clear
453 FALSE Chicago America Clear
454 FALSE Edmonton America Overcast
455 FALSE Los_Angeles America Clear
456 FALSE Mexico_City America Clear
457 TRUE New_York America MostlyCloudy
458 TRUE Madrid Europe PartlyCloudy
459 FALSE Zurich Europe PartlyCloudy
460 FALSE Paris Europe Overcast
461 FALSE Prague Europe Overcast
462 FALSE New_York America Clear
463 TRUE Prague Europe Overcast
464 FALSE London Europe Clear
465 FALSE Manila Asia MostlyCloudy
466 FALSE Chicago America PartlyCloudy
467 FALSE Oslo Europe Clear
468 FALSE Stockholm Europe MostlyCloudy
469 TRUE Stockholm Europe MostlyCloudy
470 TRUE Madrid Europe Clear
471 TRUE Chicago America Clear
472 FALSE New_York America Clear
473 TRUE New_York America Clear
474 FALSE Chicago America Clear
475 FALSE New_York America Clear
476 FALSE New_York America PartlyCloudy
477 FALSE Regina America PartlyCloudy
478 FALSE Los_Angeles America Clear
479 TRUE New_York America PartlyCloudy
480 FALSE Los_Angeles America Clear
481 FALSE Phoenix America PartlyCloudy
482 FALSE New_York America Clear
483 FALSE Edmonton America PartlyCloudy
temperature windSpeed windBearing pressure weatherIcon
1 17.0 13.07 295 1007.84 clear-day
2 24.7 15.32 2 1017.44 clear-day
3 23.3 0.84 311 1017.10 partly-cloudy-day
4 21.9 5.12 238 1012.70 partly-cloudy-day
5 28.9 6.92 105 1014.84 partly-cloudy-day
6 24.6 8.54 240 1016.33 partly-cloudy-day
7 24.5 5.00 230 1021.24 clear-night
8 24.7 15.32 2 1017.44 clear-day
9 18.0 5.03 271 1009.11 clear-night
10 16.6 3.02 49 1011.09 clear-day
11 21.1 6.02 178 1023.00 partly-cloudy-day
12 25.1 3.23 233 1016.15 clear-day
13 18.8 3.59 90 1023.04 clear-night
14 25.4 1.72 173 1012.85 partly-cloudy-day
15 29.3 8.51 143 1014.72 clear-day
16 12.8 2.99 337 1016.50 clear-night
17 18.4 12.43 32 1024.46 partly-cloudy-day
18 20.9 9.11 259 1016.87 rain
19 25.7 3.46 302 1017.74 partly-cloudy-night
20 28.9 8.02 260 1008.15 clear-day
21 28.4 7.48 1 1012.16 clear-day
22 8.2 6.47 259 1007.77 clear-night
23 27.5 6.99 180 1006.60 clear-day
24 13.4 2.14 276 1014.46 clear-night
25 16.0 9.29 301 1018.22 partly-cloudy-day
26 29.8 5.21 86 1019.64 clear-day
27 22.0 4.21 299 1017.02 partly-cloudy-day
28 24.6 3.64 90 1013.39 rain
29 15.1 4.47 234 1016.29 partly-cloudy-day
30 21.5 9.95 226 1008.51 clear-day
31 24.4 6.61 352 1013.57 cloudy
32 25.1 10.35 18 1012.70 clear-day
33 28.4 7.48 1 1012.16 clear-day
34 22.0 5.52 80 1023.92 clear-day
35 23.4 4.23 241 1017.18 partly-cloudy-night
36 31.7 5.45 121 1013.10 partly-cloudy-day
37 29.8 3.84 84 1003.65 clear-day
38 18.8 3.50 304 1007.84 partly-cloudy-night
39 22.9 0.79 139 1018.71 partly-cloudy-night
40 25.4 0.92 123 1020.69 clear-night
41 15.8 8.88 175 1009.69 cloudy
42 28.1 7.54 5 1022.47 clear-day
43 23.6 9.75 342 1017.35 clear-day
44 26.8 2.90 153 1017.78 clear-night
45 14.8 6.00 308 1020.04 partly-cloudy-night
46 7.5 2.45 96 1017.10 clear-night
47 14.5 6.45 88 1028.15 clear-night
48 27.0 6.25 148 1007.64 clear-day
49 26.6 5.25 150 1020.27 partly-cloudy-night
50 22.8 2.41 110 1023.04 clear-night
51 25.9 5.67 40 1010.08 cloudy
52 21.0 6.94 218 1019.82 partly-cloudy-day
53 24.9 0.73 353 1014.57 clear-night
54 22.1 8.20 348 1016.46 clear-day
55 15.7 13.50 233 1004.47 clear-night
56 32.0 9.12 345 1015.59 clear-day
57 13.9 5.48 358 1019.94 clear-night
58 19.3 7.67 254 1008.57 clear-day
59 17.2 5.98 105 1022.68 clear-night
60 17.4 9.08 302 1023.91 partly-cloudy-day
61 32.8 11.00 240 1008.92 clear-day
62 27.2 8.51 230 1008.29 clear-day
63 16.8 1.17 53 1020.97 partly-cloudy-day
64 12.3 5.24 110 1004.54 clear-day
65 20.0 4.35 211 1021.23 clear-night
66 19.2 15.82 21 1010.66 rain
67 10.4 12.44 243 1009.10 cloudy
68 21.3 3.00 230 1020.23 clear-night
69 21.4 3.28 262 1008.24 clear-day
70 12.3 4.38 329 1010.99 partly-cloudy-day
71 24.9 6.78 151 1005.86 clear-night
72 23.2 7.00 190 1019.47 clear-night
73 11.5 20.31 74 1014.20 clear-night
74 21.2 1.39 212 1015.26 clear-day
75 31.5 2.28 182 1020.72 clear-day
76 11.8 4.04 211 1017.35 partly-cloudy-night
77 27.6 2.48 277 1012.55 partly-cloudy-day
78 20.9 7.06 0 1025.19 partly-cloudy-day
79 24.8 3.64 166 1010.86 clear-day
80 24.9 5.79 263 1016.38 partly-cloudy-day
81 23.3 3.80 91 1020.75 clear-day
82 16.5 5.33 271 1011.67 partly-cloudy-day
83 18.1 4.62 110 1022.54 clear-night
84 14.7 7.99 170 1014.07 rain
85 20.1 0.17 38 1018.02 partly-cloudy-night
86 22.0 6.75 255 1021.76 clear-day
87 22.5 3.17 231 1003.77 rain
88 19.1 6.78 55 1021.89 clear-day
89 30.5 0.95 177 1018.55 partly-cloudy-day
90 13.3 4.38 158 1015.41 clear-night
91 14.6 5.96 8 1024.55 clear-night
92 24.4 4.65 207 1012.81 clear-day
93 30.5 7.02 86 1012.00 partly-cloudy-day
94 16.3 3.82 293 1018.22 clear-night
95 14.3 8.13 155 1012.13 clear-day
96 20.4 8.77 215 1025.97 clear-day
97 34.4 7.86 148 1007.33 clear-day
98 16.3 11.28 343 1025.83 partly-cloudy-day
99 9.8 8.84 117 1029.59 cloudy
100 12.3 4.38 329 1010.99 partly-cloudy-day
101 19.4 4.20 130 1023.30 clear-night
102 34.4 10.80 123 1001.85 clear-day
103 9.0 3.54 121 1010.36 clear-night
104 32.5 6.91 96 1014.90 clear-day
105 31.4 7.03 235 1015.49 clear-day
106 13.8 9.74 309 1028.73 cloudy
107 14.0 5.58 228 1015.96 partly-cloudy-day
108 13.8 5.26 62 1024.80 clear-night
109 33.0 10.62 119 1018.83 clear-day
110 26.7 6.05 135 1007.51 partly-cloudy-night
111 27.1 6.38 144 1017.95 clear-night
112 27.8 6.16 220 1015.59 clear-day
113 15.6 12.60 243 1009.24 partly-cloudy-day
114 17.4 13.45 290 1014.64 partly-cloudy-day
115 20.9 7.06 0 1025.19 partly-cloudy-day
116 27.6 17.42 354 1017.03 clear-day
117 25.0 9.90 101 1020.50 partly-cloudy-night
118 34.3 1.88 202 1006.44 clear-night
119 14.6 5.68 329 1016.30 clear-night
120 17.4 13.03 202 1005.28 clear-night
121 5.2 0.82 265 1011.93 partly-cloudy-night
122 10.0 11.33 1 1019.06 clear-night
123 12.8 3.34 345 1023.30 clear-night
124 26.5 7.80 201 1001.20 clear-day
125 21.8 8.98 189 1025.44 partly-cloudy-day
126 27.5 11.31 194 1007.22 partly-cloudy-day
127 21.0 12.12 312 1002.61 clear-night
128 27.6 5.48 212 1015.68 clear-day
129 24.4 16.45 251 1011.44 partly-cloudy-day
130 27.8 8.77 60 1018.17 partly-cloudy-day
131 21.8 1.33 316 1017.90 partly-cloudy-night
132 14.0 13.43 246 1006.17 partly-cloudy-night
133 18.7 9.23 340 1011.72 clear-day
134 19.8 5.23 213 1018.72 clear-night
135 21.7 4.46 301 1018.33 clear-night
136 32.1 6.95 61 1013.02 clear-day
137 24.2 5.55 164 1016.94 clear-night
138 16.5 1.88 348 1023.00 clear-day
139 21.0 16.92 49 1024.01 clear-day
140 27.9 2.79 69 1018.78 rain
141 17.4 2.57 347 1019.05 partly-cloudy-night
142 32.2 7.69 238 1010.62 clear-day
143 13.8 7.88 298 1019.78 cloudy
144 26.0 12.31 278 1014.17 partly-cloudy-day
145 24.6 7.06 200 1015.18 clear-night
146 17.6 9.48 262 1011.90 partly-cloudy-day
147 17.8 20.67 237 1012.08 wind
148 25.6 4.97 158 1018.09 partly-cloudy-night
149 29.0 8.25 1 1017.18 clear-day
150 27.1 3.96 242 1018.55 partly-cloudy-day
151 16.1 7.60 135 1010.09 partly-cloudy-day
152 20.0 2.49 7 1019.40 partly-cloudy-night
153 27.3 5.00 326 1016.26 clear-night
154 19.7 3.07 161 1022.09 cloudy
155 22.5 6.91 229 1023.87 cloudy
156 25.6 2.15 354 1012.37 clear-night
157 25.9 11.62 245 1011.87 partly-cloudy-day
158 31.2 6.49 116 1015.26 clear-day
159 26.2 7.46 67 1010.81 clear-day
160 14.1 4.88 346 1013.02 rain
161 19.2 1.89 218 1019.21 partly-cloudy-day
162 24.0 7.33 333 1024.16 clear-day
163 26.9 14.24 95 1014.19 clear-day
164 19.9 3.02 72 1023.82 clear-night
165 21.9 3.00 320 1018.30 clear-night
166 20.0 2.50 5 1019.40 partly-cloudy-night
167 18.5 3.08 134 1014.10 partly-cloudy-day
168 8.0 2.02 114 1010.11 partly-cloudy-night
169 16.6 2.69 31 1022.69 partly-cloudy-night
170 14.6 5.68 329 1016.30 clear-night
171 9.8 8.84 117 1029.59 cloudy
172 24.3 2.79 198 1015.29 clear-night
173 19.0 12.88 289 1013.19 partly-cloudy-day
174 20.5 3.86 120 1022.18 clear-night
175 28.5 8.55 74 1015.84 partly-cloudy-day
176 25.2 3.64 154 1020.14 clear-night
177 13.0 1.49 206 1005.75 rain
178 31.6 6.82 245 1015.49 partly-cloudy-night
179 36.1 4.86 316 1005.10 clear-night
180 13.6 6.00 204 1015.09 clear-day
181 27.0 11.16 21 1013.85 clear-day
182 9.0 3.54 121 1010.36 clear-night
183 29.0 6.85 284 1024.23 clear-day
184 27.0 5.26 250 1018.99 clear-night
185 28.6 7.17 62 1016.44 partly-cloudy-day
186 19.1 5.78 259 1011.36 partly-cloudy-day
187 28.5 8.42 229 1017.08 clear-night
188 15.8 10.37 305 1010.00 clear-day
189 16.3 6.93 1 1020.42 partly-cloudy-night
190 21.8 4.58 120 1022.27 clear-night
191 11.9 7.38 151 1025.46 clear-night
192 25.8 3.41 19 1019.29 partly-cloudy-night
193 22.5 6.11 225 1024.01 cloudy
194 17.6 14.31 291 1014.64 partly-cloudy-day
195 11.3 1.82 57 1003.81 clear-night
196 16.3 8.24 21 1025.89 partly-cloudy-night
197 24.6 1.34 249 1019.98 partly-cloudy-day
198 25.5 8.17 267 1016.41 partly-cloudy-day
199 24.7 3.32 41 1014.97 clear-day
200 16.8 1.90 173 1014.60 clear-day
201 10.5 1.16 159 1011.85 partly-cloudy-day
202 17.5 10.15 32 1025.24 clear-night
203 27.2 6.30 91 1014.07 clear-night
204 20.5 6.40 245 1024.10 cloudy
205 30.1 10.88 217 1016.82 clear-day
206 17.5 12.69 207 1005.19 clear-night
207 17.4 4.06 310 1017.65 partly-cloudy-day
208 26.6 5.25 150 1020.27 partly-cloudy-night
209 23.0 5.65 289 1017.66 cloudy
210 22.7 6.20 260 1013.61 clear-night
211 18.7 9.23 340 1011.72 clear-day
212 7.8 2.81 43 1029.14 clear-night
213 27.9 8.55 202 1016.29 partly-cloudy-night
214 14.8 5.12 352 1020.00 partly-cloudy-night
215 23.1 7.00 257 1023.13 partly-cloudy-day
216 17.4 3.17 107 1009.88 partly-cloudy-night
217 16.2 3.97 103 1007.00 cloudy
218 16.4 2.27 59 1012.35 clear-night
219 14.4 12.82 239 1005.02 partly-cloudy-night
220 25.8 3.29 23 1019.32 clear-night
221 16.1 11.16 150 1016.06 clear-day
222 5.0 0.70 315 1011.75 partly-cloudy-night
223 27.3 4.42 90 1012.44 partly-cloudy-day
224 19.4 8.40 52 1025.92 partly-cloudy-day
225 30.2 6.63 242 1019.03 clear-day
226 16.8 12.91 288 1014.19 partly-cloudy-day
227 23.7 3.00 340 1021.10 clear-day
228 8.2 6.47 259 1007.77 clear-night
229 13.8 7.88 298 1019.78 cloudy
230 18.0 15.33 191 1022.75 partly-cloudy-day
231 14.3 8.13 155 1012.13 clear-day
232 19.3 3.35 190 1021.63 partly-cloudy-night
233 25.3 6.00 180 1025.63 clear-day
234 15.4 1.60 226 1014.98 clear-night
235 21.2 6.00 180 1022.19 clear-night
236 24.0 5.98 160 1012.90 clear-night
237 15.2 6.82 206 1016.85 partly-cloudy-day
238 22.4 6.39 85 1018.40 clear-night
239 23.3 5.05 335 1019.15 clear-night
240 14.1 6.07 210 1025.00 clear-night
241 16.1 9.29 12 1024.81 partly-cloudy-day
242 19.9 3.02 72 1023.82 clear-night
243 12.4 4.86 23 1014.90 rain
244 22.7 9.93 128 1019.71 clear-day
245 23.3 4.07 271 1016.81 partly-cloudy-day
246 17.6 2.15 206 1015.66 partly-cloudy-day
247 17.1 5.86 198 1017.03 partly-cloudy-night
248 22.3 6.33 133 1023.43 clear-day
249 27.9 7.14 58 1017.58 partly-cloudy-day
250 14.0 3.82 171 1015.86 partly-cloudy-night
251 15.1 12.80 330 1017.57 partly-cloudy-night
252 16.5 9.94 341 1023.55 partly-cloudy-day
253 17.5 3.00 130 1014.14 partly-cloudy-night
254 22.5 6.11 225 1024.01 cloudy
255 16.3 6.40 277 1014.21 partly-cloudy-day
256 25.2 2.14 209 1017.04 partly-cloudy-day
257 17.9 15.26 192 1023.09 partly-cloudy-day
258 21.8 1.68 11 1002.88 clear-night
259 24.9 5.23 233 1017.38 partly-cloudy-day
260 19.1 5.97 253 1011.04 partly-cloudy-night
261 13.8 10.20 252 1007.51 partly-cloudy-night
262 32.2 4.72 147 1011.15 clear-day
263 18.0 4.08 53 1022.01 clear-night
264 28.0 5.22 148 1021.27 clear-day
265 17.8 4.34 188 1010.68 clear-night
266 26.1 6.50 120 1007.62 cloudy
267 13.0 1.04 166 1009.98 partly-cloudy-day
268 23.4 8.32 21 1025.40 partly-cloudy-day
269 9.6 8.98 39 1013.92 clear-night
270 17.7 4.92 204 1011.42 clear-day
271 20.5 4.91 284 1020.28 clear-day
272 26.1 6.14 314 1012.47 partly-cloudy-day
273 14.1 12.49 320 1017.82 rain
274 31.4 3.72 143 1011.51 partly-cloudy-day
275 10.8 15.04 99 1029.16 cloudy
276 24.3 2.79 198 1015.29 clear-night
277 25.6 7.33 100 1010.89 partly-cloudy-night
278 13.1 4.54 116 1019.86 clear-night
279 26.3 3.03 66 1017.03 partly-cloudy-day
280 8.9 14.10 219 1015.19 wind
281 12.9 5.64 313 1012.27 clear-day
282 27.3 3.23 336 1019.48 clear-day
283 16.4 1.09 116 1009.90 clear-night
284 26.3 4.01 13 1019.86 clear-night
285 22.9 7.52 102 1022.52 clear-day
286 7.1 2.34 120 1016.61 clear-night
287 28.2 3.51 136 1017.68 clear-day
288 25.1 10.75 222 1010.28 clear-day
289 18.1 4.19 241 1023.78 cloudy
290 33.0 9.37 164 1017.00 clear-day
291 30.6 3.54 34 1020.02 clear-day
292 20.8 6.53 79 1021.21 clear-day
293 26.7 6.05 135 1007.51 partly-cloudy-night
294 24.1 5.10 204 1012.45 partly-cloudy-night
295 11.5 3.19 280 1016.68 clear-night
296 23.8 4.12 162 1017.92 clear-night
297 15.7 11.39 18 1024.20 partly-cloudy-day
298 25.8 3.29 23 1019.32 clear-night
299 15.9 3.35 208 1011.65 partly-cloudy-night
300 29.6 5.13 24 1013.15 clear-day
301 17.1 7.33 261 1012.56 rain
302 7.1 2.34 120 1016.61 clear-night
303 27.2 10.10 79 1017.95 partly-cloudy-day
304 17.1 4.41 174 1018.81 cloudy
305 17.2 15.75 257 1013.40 rain
306 25.8 5.57 44 1012.07 cloudy
307 19.1 4.28 178 1021.05 partly-cloudy-night
308 20.6 10.24 35 1023.42 clear-night
309 26.3 7.45 74 1017.16 partly-cloudy-day
310 30.0 10.34 311 1007.32 clear-day
311 18.6 3.56 81 1014.34 clear-night
312 19.0 10.49 294 1013.47 clear-day
313 32.6 18.57 215 1014.02 wind
314 9.4 13.60 92 1001.75 clear-day
315 31.0 12.40 246 1019.70 clear-day
316 19.9 4.18 4 1022.51 clear-night
317 13.2 3.35 110 1019.13 clear-night
318 31.8 13.46 218 1014.26 partly-cloudy-day
319 30.8 5.68 228 1012.21 partly-cloudy-day
320 22.8 5.21 158 1019.94 clear-night
321 17.6 15.64 267 1009.44 cloudy
322 8.9 14.10 219 1015.19 wind
323 17.2 2.52 234 1019.19 cloudy
324 27.8 4.99 60 1019.31 rain
325 17.4 4.06 310 1017.65 partly-cloudy-day
326 10.8 10.83 323 1007.22 clear-day
327 14.7 0.44 350 1023.73 clear-night
328 14.8 3.34 228 1011.80 partly-cloudy-night
329 25.1 10.75 222 1010.28 clear-day
330 17.5 12.69 207 1005.19 clear-night
331 26.0 10.54 61 1021.17 partly-cloudy-day
332 12.4 7.28 23 1007.78 clear-night
333 18.4 4.64 212 1018.58 partly-cloudy-night
334 26.8 9.89 48 1019.59 clear-day
335 14.6 1.90 142 1021.84 clear-night
336 25.8 1.88 188 1019.64 clear-night
337 20.5 3.86 120 1022.18 clear-night
338 27.4 13.83 346 1015.68 clear-day
339 30.3 6.39 200 1007.29 partly-cloudy-night
340 27.8 6.16 220 1015.59 clear-day
341 20.3 5.16 183 1020.95 clear-night
342 22.4 4.40 258 1011.91 cloudy
343 25.5 2.90 110 1014.33 clear-night
344 26.2 6.00 20 1009.90 cloudy
345 24.2 2.39 345 1016.65 partly-cloudy-night
346 26.7 1.01 280 1018.00 clear-night
347 18.3 3.42 208 1017.27 partly-cloudy-night
348 19.7 4.27 1 1023.54 partly-cloudy-day
349 22.6 4.50 199 1017.30 clear-day
350 17.7 4.14 106 1024.33 clear-night
351 10.2 10.50 310 1008.59 partly-cloudy-day
352 19.9 8.99 3 1023.02 clear-day
353 27.8 9.32 92 1005.74 partly-cloudy-night
354 9.2 1.90 328 1018.99 clear-night
355 17.0 9.74 252 1011.80 partly-cloudy-day
356 21.8 6.10 93 1022.44 clear-night
357 18.9 6.22 9 1025.11 clear-day
358 19.1 8.07 114 1019.18 partly-cloudy-night
359 20.4 8.77 215 1025.97 clear-day
360 12.7 0.62 60 1025.76 clear-night
361 27.9 6.18 72 1020.35 partly-cloudy-day
362 14.6 5.67 305 1009.86 clear-day
363 20.7 5.87 204 1019.76 clear-day
364 17.9 12.04 298 1015.98 partly-cloudy-day
365 26.7 3.63 208 1005.60 cloudy
366 17.3 5.21 213 1017.06 partly-cloudy-night
367 9.2 1.90 328 1018.99 clear-night
368 20.7 5.91 185 1022.08 clear-night
369 17.4 9.30 284 1014.71 clear-day
370 26.8 2.99 123 1013.43 clear-night
371 27.7 3.61 146 1020.83 clear-night
372 32.7 6.25 187 1010.53 clear-day
373 18.4 3.62 250 1021.62 clear-night
374 12.9 1.16 111 1013.87 clear-night
375 23.7 3.00 340 1021.10 clear-day
376 12.1 4.09 224 1016.98 clear-night
377 19.8 10.90 199 1019.87 cloudy
378 18.5 0.60 319 1023.74 partly-cloudy-day
379 27.4 9.27 54 1014.01 partly-cloudy-day
380 15.7 2.41 90 1024.10 clear-night
381 23.6 1.90 170 1021.91 clear-night
382 31.1 7.33 347 1017.06 partly-cloudy-day
383 14.2 5.37 101 1028.06 clear-night
384 12.2 0.08 155 1015.68 clear-night
385 16.3 8.24 21 1025.89 partly-cloudy-night
386 9.9 1.97 90 1026.50 clear-night
387 24.6 3.64 90 1013.39 rain
388 20.4 6.38 303 1004.46 clear-night
389 18.1 3.12 117 1023.20 clear-night
390 25.1 10.75 222 1010.28 clear-day
391 25.2 7.40 81 1020.26 partly-cloudy-day
392 24.1 9.63 218 1008.15 clear-day
393 24.9 8.21 53 1016.20 clear-night
394 26.5 3.12 188 1015.95 clear-night
395 16.4 7.49 43 1022.30 clear-night
396 29.4 8.83 204 1015.01 clear-night
397 13.4 3.00 160 1026.01 clear-night
398 21.7 6.97 120 1016.04 clear-night
399 28.2 7.45 114 1017.18 partly-cloudy-day
400 19.0 10.44 183 1017.49 clear-day
401 22.9 1.65 76 1009.12 clear-night
402 16.4 18.04 294 1009.90 clear-day
403 18.7 7.29 185 1017.30 clear-day
404 24.5 6.70 208 1013.15 partly-cloudy-day
405 22.9 5.05 15 1015.76 rain
406 18.0 5.44 48 1018.44 clear-day
407 16.0 11.66 302 1015.36 partly-cloudy-day
408 21.8 1.68 11 1002.88 clear-night
409 26.6 5.25 150 1020.27 partly-cloudy-night
410 21.0 2.39 76 1018.32 partly-cloudy-night
411 27.7 5.73 288 1007.46 cloudy
412 31.3 4.58 125 1016.28 clear-day
413 22.6 8.55 212 1004.24 clear-night
414 17.9 6.21 214 1025.20 clear-day
415 11.7 0.00 172 1026.06 partly-cloudy-day
416 17.5 5.68 277 1010.40 clear-night
417 14.7 0.49 109 1008.72 clear-day
418 12.9 3.00 70 1021.24 clear-night
419 22.2 5.26 174 1002.90 clear-night
420 22.9 3.62 101 1023.02 clear-night
421 26.1 10.27 45 1019.18 partly-cloudy-day
422 24.0 10.96 270 1009.49 clear-day
423 12.8 4.98 340 1013.11 rain
424 25.9 11.25 245 1012.39 partly-cloudy-day
425 23.6 6.83 322 1012.72 partly-cloudy-day
426 18.0 9.71 352 1004.93 clear-night
427 17.3 2.22 255 1019.28 clear-night
428 20.1 13.30 216 1024.33 clear-day
429 15.9 5.98 20 1025.84 clear-night
430 17.4 18.60 23 1015.67 rain
431 17.9 8.53 208 1005.23 clear-night
432 20.0 7.95 273 1005.52 partly-cloudy-day
433 22.9 11.54 232 1012.14 partly-cloudy-day
434 25.7 10.85 130 1015.90 partly-cloudy-day
435 16.2 2.11 215 1015.15 clear-day
436 27.0 1.79 155 1011.65 clear-day
437 26.0 8.06 54 1019.45 clear-day
438 19.4 10.15 223 1015.93 clear-day
439 14.8 10.74 147 1016.20 clear-day
440 28.6 5.98 200 1007.91 partly-cloudy-night
441 25.6 4.98 270 1015.60 clear-night
442 15.1 4.67 326 1010.20 clear-day
443 27.5 7.52 210 1015.37 clear-night
444 8.3 6.17 269 1007.69 clear-night
445 26.1 3.49 140 1020.14 clear-night
446 27.4 5.13 92 1013.23 clear-night
447 12.0 14.06 10 1019.72 rain
448 20.5 4.74 126 1014.19 partly-cloudy-day
449 26.7 3.00 320 1013.30 clear-night
450 15.8 5.55 191 1020.67 partly-cloudy-night
451 27.6 7.73 176 1016.64 clear-night
452 21.6 6.71 321 1017.90 clear-night
453 24.9 3.26 113 1016.73 clear-night
454 7.3 2.47 119 1016.54 cloudy
455 16.6 3.08 297 1018.50 clear-night
456 11.2 5.00 20 1010.84 clear-day
457 25.3 7.62 28 1018.23 partly-cloudy-day
458 27.8 2.84 269 1016.96 partly-cloudy-night
459 25.6 11.06 16 1012.86 partly-cloudy-day
460 17.2 3.47 115 1021.16 cloudy
461 16.1 4.61 27 1026.73 cloudy
462 27.0 5.98 100 1021.19 clear-night
463 22.4 6.63 260 1011.52 cloudy
464 19.7 7.74 117 1014.18 clear-night
465 31.2 4.76 305 1011.27 partly-cloudy-day
466 31.9 9.26 233 1022.12 partly-cloudy-day
467 11.7 3.43 207 1016.43 clear-night
468 17.3 1.48 122 1008.41 partly-cloudy-day
469 17.1 8.78 255 1011.78 partly-cloudy-day
470 22.9 3.36 304 1018.95 clear-night
471 26.8 2.90 153 1017.78 clear-night
472 21.1 2.96 68 1024.60 clear-night
473 25.0 7.88 39 1023.92 clear-day
474 27.5 6.58 132 1017.54 clear-night
475 19.1 6.78 55 1021.89 clear-day
476 23.3 15.28 19 1016.27 partly-cloudy-night
477 20.8 14.31 259 1007.75 partly-cloudy-day
478 25.3 19.58 232 1007.68 clear-day
479 24.6 6.54 199 1015.01 partly-cloudy-night
480 35.7 7.10 345 1011.38 clear-day
481 31.1 3.50 146 1007.13 partly-cloudy-day
482 29.8 5.34 84 1019.64 clear-day
483 6.8 3.85 339 1020.80 partly-cloudy-day
sunriseMinutesMidnight sunriseHour sunriseMinute sunriseMinutesSince
1 377 6 17 296
2 412 6 52 187
3 384 6 24 282
4 382 6 22 301
5 402 6 42 735
6 380 6 20 697
7 436 7 16 883
8 412 6 52 187
9 389 6 29 1046
10 395 6 35 92
11 391 6 31 688
12 409 6 49 725
13 424 7 4 970
14 410 6 50 302
15 424 7 4 583
16 399 6 39 -340
17 378 6 18 480
18 433 7 13 526
19 439 7 19 1000
20 367 6 7 1072
21 411 6 51 548
22 408 6 48 -289
23 369 6 9 950
24 377 6 17 881
25 385 6 25 782
26 435 7 15 652
27 381 6 21 744
28 409 6 49 885
29 377 6 17 758
30 389 6 29 721
31 417 6 57 -364
32 436 7 16 278
33 411 6 51 548
34 406 6 46 732
35 376 6 16 907
36 445 7 25 562
37 348 5 48 181
38 391 6 31 947
39 436 7 16 -383
40 405 6 45 1034
41 410 6 50 -266
42 403 6 43 376
43 406 6 46 681
44 401 6 41 1018
45 385 6 25 933
46 411 6 51 -337
47 387 6 27 932
48 392 6 32 719
49 401 6 41 983
50 403 6 43 796
51 435 7 15 553
52 442 7 22 262
53 422 7 2 971
54 405 6 45 408
55 381 6 21 1021
56 404 6 44 726
57 366 6 6 951
58 386 6 26 745
59 430 7 10 978
60 380 6 20 756
61 367 6 7 340
62 385 6 25 964
63 390 6 30 723
64 336 5 36 610
65 390 6 30 839
66 418 6 58 705
67 404 6 44 263
68 408 6 48 910
69 398 6 38 500
70 414 6 54 785
71 362 6 2 -317
72 437 7 17 973
73 401 6 41 -342
74 400 6 40 -341
75 436 7 16 696
76 377 6 17 -264
77 386 6 26 569
78 386 6 26 573
79 376 6 16 32
80 381 6 21 578
81 428 7 8 755
82 376 6 16 750
83 417 6 57 -375
84 394 6 34 504
85 386 6 26 913
86 408 6 48 130
87 376 6 16 823
88 431 7 11 156
89 440 7 20 223
90 392 6 32 -333
91 401 6 41 -2
92 401 6 41 -342
93 436 7 16 546
94 400 6 40 885
95 384 6 24 400
96 382 6 22 577
97 377 6 17 627
98 386 6 26 213
99 400 6 40 1024
100 414 6 54 785
101 404 6 44 792
102 363 6 3 176
103 414 6 54 -295
104 376 6 16 398
105 419 6 59 660
106 401 6 41 405
107 396 6 36 181
108 378 6 18 934
109 423 7 3 476
110 369 6 9 1033
111 424 7 4 908
112 412 6 52 392
113 378 6 18 168
114 386 6 26 288
115 386 6 26 573
116 388 6 28 561
117 335 5 35 979
118 366 6 6 848
119 398 6 38 921
120 376 6 16 -334
121 414 6 54 965
122 398 6 38 981
123 391 6 31 -167
124 419 6 59 362
125 421 7 1 313
126 418 6 58 240
127 419 6 59 959
128 412 6 52 361
129 384 6 24 530
130 396 6 36 415
131 361 6 1 -199
132 407 6 47 971
133 421 7 1 326
134 388 6 28 870
135 404 6 44 -357
136 436 7 16 462
137 384 6 24 920
138 411 6 51 780
139 387 6 27 210
140 423 7 3 419
141 391 6 31 1045
142 429 7 9 529
143 384 6 24 935
144 362 6 2 595
145 405 6 45 910
146 375 6 15 370
147 386 6 26 993
148 432 7 12 -379
149 448 7 28 656
150 406 6 46 457
151 381 6 21 472
152 466 7 46 -227
153 403 6 43 912
154 434 7 14 705
155 384 6 24 362
156 435 7 15 996
157 381 6 21 460
158 400 6 40 559
159 435 7 15 463
160 370 6 10 318
161 372 6 12 222
162 455 7 35 648
163 445 7 25 943
164 402 6 42 903
165 416 6 56 -316
166 465 7 45 -226
167 389 6 29 236
168 414 6 54 -184
169 390 6 30 922
170 398 6 38 921
171 400 6 40 1024
172 405 6 45 1016
173 377 6 17 696
174 436 7 16 -282
175 398 6 38 446
176 401 6 41 -42
177 371 6 11 732
178 466 7 46 811
179 367 6 7 771
180 376 6 16 114
181 412 6 52 695
182 414 6 54 -295
183 436 7 16 285
184 416 6 56 776
185 383 6 23 297
186 392 6 32 207
187 431 7 11 766
188 385 6 25 334
189 362 6 2 932
190 436 7 16 -405
191 402 6 42 -4
192 424 7 4 -348
193 382 6 22 331
194 384 6 24 312
195 337 5 37 930
196 378 6 18 936
197 434 7 14 263
198 381 6 21 576
199 412 6 52 742
200 390 6 30 741
201 380 6 20 133
202 374 6 14 939
203 436 7 16 821
204 382 6 22 157
205 383 6 23 333
206 376 6 16 1063
207 400 6 40 738
208 401 6 41 983
209 421 7 1 638
210 410 6 50 -406
211 421 7 1 326
212 401 6 41 982
213 384 6 24 1000
214 373 6 13 999
215 381 6 21 518
216 388 6 28 -255
217 381 6 21 689
218 440 7 20 995
219 407 6 47 851
220 424 7 4 -365
221 387 6 27 246
222 414 6 54 1019
223 435 7 15 704
224 389 6 29 627
225 412 6 52 667
226 379 6 19 315
227 411 6 51 728
228 408 6 48 -289
229 384 6 24 935
230 385 6 25 719
231 384 6 24 400
232 379 6 19 1000
233 379 6 19 554
234 387 6 27 858
235 420 7 0 869
236 435 7 15 1004
237 370 6 10 87
238 401 6 41 810
239 404 6 44 -402
240 380 6 20 979
241 386 6 26 368
242 402 6 42 903
243 402 6 42 677
244 381 6 21 758
245 383 6 23 706
246 377 6 17 134
247 381 6 21 999
248 380 6 20 338
249 396 6 36 383
250 395 6 35 -382
251 377 6 17 997
252 386 6 26 46
253 460 7 40 -309
254 382 6 22 331
255 408 6 48 310
256 404 6 44 375
257 384 6 24 699
258 393 6 33 806
259 381 6 21 397
260 369 6 9 920
261 408 6 48 -356
262 395 6 35 383
263 380 6 20 888
264 430 7 10 620
265 387 6 27 886
266 368 6 8 -256
267 378 6 18 78
268 357 5 57 559
269 383 6 23 -379
270 404 6 44 373
271 405 6 45 39
272 401 6 41 267
273 380 6 20 927
274 445 7 25 687
275 427 7 7 -284
276 405 6 45 1016
277 444 7 24 850
278 383 6 23 1017
279 404 6 44 555
280 424 7 4 950
281 374 6 14 380
282 381 6 21 502
283 387 6 27 -346
284 404 6 44 795
285 381 6 21 461
286 412 6 52 -293
287 381 6 21 696
288 388 6 28 511
289 393 6 33 -230
290 439 7 19 448
291 401 6 41 492
292 380 6 20 726
293 369 6 9 1033
294 401 6 41 -287
295 399 6 39 -280
296 434 7 14 825
297 378 6 18 371
298 424 7 4 -365
299 379 6 19 -321
300 412 6 52 547
301 384 6 24 -351
302 412 6 52 -293
303 394 6 34 624
304 412 6 52 965
305 447 7 27 -88
306 435 7 15 702
307 379 6 19 873
308 388 6 28 -360
309 405 6 45 312
310 400 6 40 584
311 424 7 4 892
312 385 6 25 507
313 383 6 23 635
314 348 5 48 611
315 430 7 10 649
316 406 6 46 882
317 399 6 39 842
318 384 6 24 724
319 377 6 17 315
320 381 6 21 781
321 413 6 53 690
322 424 7 4 950
323 390 6 30 389
324 431 7 11 452
325 400 6 40 738
326 340 5 40 405
327 404 6 44 -107
328 376 6 16 943
329 388 6 28 511
330 376 6 16 1063
331 430 7 10 478
332 383 6 23 844
333 384 6 24 855
334 427 7 7 709
335 359 5 59 1079
336 437 7 17 865
337 436 7 16 -282
338 388 6 28 751
339 367 6 7 779
340 412 6 52 392
341 381 6 21 1038
342 384 6 24 393
343 376 6 16 -204
344 435 7 15 584
345 404 6 44 -345
346 436 7 16 1003
347 367 6 7 929
348 402 6 42 78
349 402 6 42 342
350 413 6 53 -389
351 340 5 40 517
352 388 6 28 200
353 369 6 9 832
354 399 6 39 844
355 375 6 15 224
356 419 6 59 870
357 374 6 14 224
358 388 6 28 957
359 382 6 22 577
360 380 6 20 -248
361 430 7 10 227
362 414 6 54 561
363 378 6 18 665
364 384 6 24 574
365 354 5 54 825
366 381 6 21 1058
367 399 6 39 844
368 381 6 21 1040
369 387 6 27 690
370 435 7 15 -334
371 436 7 16 756
372 421 7 1 358
373 408 6 48 -350
374 396 6 36 -357
375 411 6 51 728
376 376 6 16 -197
377 412 6 52 445
378 415 6 55 4
379 415 6 55 601
380 403 6 43 -229
381 429 7 9 770
382 405 6 45 734
383 387 6 27 1029
384 392 6 32 923
385 378 6 18 936
386 391 6 31 0
387 409 6 49 885
388 392 6 32 907
389 429 7 9 949
390 388 6 28 511
391 360 6 0 266
392 389 6 29 566
393 315 5 15 1064
394 385 6 25 874
395 416 6 56 966
396 383 6 23 867
397 380 6 20 -321
398 433 7 13 -375
399 401 6 41 726
400 379 6 19 495
401 356 5 56 -112
402 388 6 28 211
403 402 6 42 197
404 354 5 54 285
405 392 6 32 992
406 383 6 23 298
407 387 6 27 456
408 393 6 33 806
409 401 6 41 983
410 412 6 52 1017
411 343 5 43 221
412 396 6 36 656
413 393 6 33 803
414 381 6 21 758
415 379 6 19 -283
416 393 6 33 914
417 380 6 20 399
418 381 6 21 -322
419 377 6 17 -166
420 401 6 41 798
421 401 6 41 668
422 386 6 26 392
423 445 7 25 994
424 381 6 21 398
425 405 6 45 544
426 420 7 0 907
427 434 7 14 -315
428 385 6 25 394
429 375 6 15 943
430 376 6 16 -137
431 385 6 25 873
432 379 6 19 578
433 368 6 8 559
434 410 6 50 729
435 392 6 32 747
436 378 6 18 -334
437 401 6 41 656
438 376 6 16 316
439 387 6 27 152
440 366 6 6 849
441 436 7 16 1003
442 414 6 54 659
443 383 6 23 1043
444 408 6 48 -246
445 401 6 41 1037
446 445 7 25 897
447 370 6 10 23
448 389 6 29 315
449 435 7 15 1004
450 377 6 17 991
451 409 6 49 810
452 403 6 43 -175
453 409 6 49 1014
454 412 6 52 -230
455 396 6 36 923
456 445 7 25 992
457 397 6 37 755
458 439 7 19 799
459 411 6 51 301
460 437 7 17 84
461 386 6 26 915
462 435 7 15 764
463 384 6 24 470
464 382 6 22 840
465 345 5 45 319
466 386 6 26 465
467 383 6 23 879
468 377 6 17 371
469 376 6 16 216
470 444 7 24 -336
471 401 6 41 1018
472 412 6 52 907
473 384 6 24 453
474 423 7 3 831
475 431 7 11 156
476 381 6 21 925
477 383 6 23 574
478 389 6 29 372
479 424 7 4 -186
480 396 6 36 593
481 364 6 4 955
482 435 7 15 644
483 410 6 50 248
sunsetMinutesMidnight sunsetHour sunsetMinute sunsetMinutesBefore
1 1186 19 46 513
2 1195 19 55 596
3 1173 19 33 507
4 1182 19 42 499
5 1187 19 47 50
6 1183 19 43 106
7 1197 19 57 -122
8 1195 19 55 596
9 1153 19 13 -282
10 1157 19 17 670
11 1183 19 43 104
12 1200 20 0 66
13 1200 20 0 -194
14 1195 19 55 483
15 1195 19 55 188
16 1182 19 42 1123
17 1169 19 29 311
18 1228 20 28 269
19 1219 20 19 -220
20 1109 18 29 -330
21 1196 19 56 237
22 1220 20 20 1101
23 1118 18 38 -201
24 1186 19 46 -72
25 1180 19 40 13
26 1200 20 0 113
27 1181 19 41 56
28 1183 19 43 -111
29 1181 19 41 46
30 1154 19 14 44
31 1184 19 44 1131
32 1184 19 44 470
33 1196 19 56 237
34 1189 19 49 51
35 1173 19 33 -110
36 1196 19 56 189
37 1103 18 23 574
38 1169 19 29 -169
39 1181 19 41 1128
40 1161 19 21 -278
41 1211 20 11 1067
42 1174 19 34 395
43 1175 19 35 88
44 1160 19 20 -259
45 1180 19 40 -138
46 1214 20 14 1140
47 1179 19 39 -140
48 1154 19 14 43
49 1158 19 18 -226
50 1177 19 37 -22
51 1184 19 44 196
52 1241 20 41 537
53 1185 19 45 -208
54 1187 19 47 374
55 1194 19 54 -208
56 1173 19 33 43
57 1169 19 29 -148
58 1148 19 8 17
59 1205 20 5 -203
60 1167 19 27 31
61 1108 18 28 401
62 1134 18 54 -215
63 1172 19 32 59
64 1228 20 28 282
65 1190 19 50 -39
66 1189 19 49 66
67 1086 18 6 419
68 1171 19 31 -147
69 1188 19 48 290
70 1213 20 13 14
71 1125 18 45 1080
72 1196 19 56 -214
73 1174 19 34 1115
74 1185 19 45 1126
75 1198 19 58 66
76 1176 19 36 1063
77 1180 19 40 225
78 1177 19 37 218
79 1107 18 27 699
80 1181 19 41 222
81 1205 20 5 22
82 1197 19 57 71
83 1196 19 56 1154
84 1194 19 54 296
85 1177 19 37 -122
86 1171 19 31 633
87 1181 19 41 -18
88 1207 20 7 620
89 1217 20 17 554
90 1190 19 50 1131
91 1181 19 41 782
92 1183 19 43 1124
93 1183 19 43 201
94 1180 19 40 -105
95 1193 19 53 409
96 1190 19 50 231
97 1142 19 2 138
98 1177 19 37 578
99 1089 18 9 -335
100 1213 20 13 14
101 1190 19 50 -6
102 1113 18 33 574
103 1213 20 13 1094
104 1121 18 41 347
105 1189 19 49 110
106 1088 18 8 282
107 1192 19 52 615
108 1165 19 25 -147
109 1179 19 39 280
110 1118 18 38 -284
111 1179 19 39 -153
112 1193 19 53 389
113 1206 20 6 660
114 1181 19 41 507
115 1177 19 37 218
116 1163 19 23 214
117 1051 17 31 -263
118 1127 18 47 -87
119 1184 19 44 -135
120 1184 19 44 1142
121 1213 20 13 -166
122 1082 18 2 -297
123 1181 19 41 957
124 1195 19 55 414
125 1218 20 18 484
126 1196 19 56 538
127 1198 19 58 -180
128 1193 19 53 420
129 1183 19 43 269
130 1184 19 44 373
131 1065 17 45 903
132 1215 20 15 -163
133 1194 19 54 447
134 1192 19 52 -66
135 1172 19 32 1125
136 1183 19 43 285
137 1161 19 21 -143
138 1177 19 37 -14
139 1164 19 24 567
140 1177 19 37 335
141 1170 19 30 -266
142 1159 19 19 201
143 1177 19 37 -142
144 1173 19 33 216
145 1172 19 32 -143
146 1197 19 57 452
147 1190 19 50 -189
148 1187 19 47 1134
149 1237 20 37 133
150 1171 19 31 308
151 1220 20 20 367
152 1245 20 45 1006
153 1172 19 32 -143
154 1226 20 26 87
155 1178 19 38 432
156 1185 19 45 -246
157 1178 19 38 337
158 1185 19 45 226
159 1185 19 45 287
160 1164 19 24 476
161 1172 19 32 578
162 1236 20 36 133
163 1196 19 56 -192
164 1176 19 36 -129
165 1202 20 2 1102
166 1245 20 45 1006
167 1175 19 35 550
168 1213 20 13 983
169 1172 19 32 -140
170 1184 19 44 -135
171 1089 18 9 -335
172 1172 19 32 -249
173 1181 19 41 108
174 1198 19 58 1044
175 1182 19 42 338
176 1158 19 18 799
177 1227 20 27 124
178 1243 20 43 -34
179 1128 18 48 -10
180 1188 19 48 698
181 1195 19 55 88
182 1213 20 13 1094
183 1197 19 57 476
184 1181 19 41 -11
185 1145 19 5 465
186 1154 19 14 555
187 1199 19 59 2
188 1194 19 54 475
189 1169 19 29 -125
190 1198 19 58 1167
191 1207 20 7 809
192 1175 19 35 1099
193 1177 19 37 464
194 1179 19 39 483
195 1228 20 28 -39
196 1167 19 27 -147
197 1228 20 28 531
198 1184 19 44 227
199 1194 19 54 40
200 1192 19 52 61
201 1217 20 17 704
202 1154 19 14 -159
203 1183 19 43 -74
204 1177 19 37 638
205 1158 19 18 442
206 1188 19 48 -251
207 1182 19 42 44
208 1158 19 18 -226
209 1210 20 10 151
210 1195 19 55 1191
211 1194 19 54 447
212 1087 18 7 -296
213 1158 19 18 -226
214 1170 19 30 -202
215 1177 19 37 278
216 1148 19 8 1015
217 1197 19 57 127
218 1222 20 22 -213
219 1215 20 15 -43
220 1175 19 35 1116
221 1192 19 52 559
222 1213 20 13 -220
223 1182 19 42 43
224 1181 19 41 165
225 1176 19 36 97
226 1174 19 34 480
227 1177 19 37 38
228 1220 20 20 1101
229 1177 19 37 -142
230 1198 19 58 94
231 1193 19 53 409
232 1179 19 39 -200
233 1163 19 23 230
234 1195 19 55 -50
235 1185 19 45 -104
236 1191 19 51 -248
237 1166 19 26 709
238 1179 19 39 -32
239 1174 19 34 1172
240 1188 19 48 -171
241 1175 19 35 421
242 1176 19 36 -129
243 1185 19 45 106
244 1161 19 21 22
245 1182 19 42 93
246 1175 19 35 664
247 1183 19 43 -197
248 1163 19 23 445
249 1184 19 44 405
250 1183 19 43 1170
251 1169 19 29 -205
252 1166 19 26 734
253 1206 20 6 1055
254 1177 19 37 464
255 1213 20 13 495
256 1183 19 43 404
257 1197 19 57 114
258 1168 19 28 -31
259 1181 19 41 403
260 1166 19 26 -123
261 1213 20 13 1161
262 1123 18 43 345
263 1161 19 21 -107
264 1205 20 5 155
265 1154 19 14 -119
266 1107 18 27 995
267 1194 19 54 738
268 1154 19 14 238
269 1210 20 10 1206
270 1175 19 35 398
271 1175 19 35 731
272 1183 19 43 515
273 1172 19 32 -135
274 1196 19 56 64
275 1119 18 39 976
276 1172 19 32 -249
277 1198 19 58 -96
278 1179 19 39 -221
279 1183 19 43 224
280 1117 18 37 -257
281 1223 20 23 469
282 1159 19 19 276
283 1147 19 7 1106
284 1177 19 37 -22
285 1163 19 23 321
286 1215 20 15 1096
287 1150 19 10 73
288 1155 19 15 256
289 1185 19 45 1022
290 1219 20 19 332
291 1158 19 18 265
292 1163 19 23 57
293 1118 18 38 -284
294 1183 19 43 1069
295 1182 19 42 1063
296 1201 20 1 -58
297 1169 19 29 420
298 1175 19 35 1116
299 1174 19 34 1116
300 1195 19 55 236
301 1177 19 37 1144
302 1215 20 15 1096
303 1169 19 29 151
304 1208 20 8 -169
305 1244 20 44 885
306 1185 19 45 48
307 1179 19 39 -73
308 1163 19 23 1135
309 1183 19 43 466
310 1170 19 30 186
311 1206 20 6 -110
312 1190 19 50 298
313 1158 19 18 140
314 1215 20 15 256
315 1201 20 1 122
316 1174 19 34 -114
317 1183 19 43 -58
318 1158 19 18 50
319 1109 18 29 417
320 1165 19 25 3
321 1218 20 18 115
322 1117 18 37 -257
323 1172 19 32 393
324 1181 19 41 298
325 1182 19 42 44
326 1223 20 23 478
327 1175 19 35 878
328 1197 19 57 -122
329 1155 19 15 256
330 1188 19 48 -251
331 1207 20 7 299
332 1166 19 26 -61
333 1189 19 49 -50
334 1184 19 44 48
335 1151 19 11 -287
336 1195 19 55 -107
337 1198 19 58 1044
338 1163 19 23 24
339 1109 18 29 -37
340 1193 19 53 389
341 1181 19 41 -238
342 1184 19 44 407
343 1120 18 40 948
344 1185 19 45 166
345 1171 19 31 1112
346 1182 19 42 -257
347 1168 19 28 -128
348 1176 19 36 696
349 1181 19 41 437
350 1187 19 47 1163
351 1223 20 23 366
352 1163 19 23 575
353 1120 18 40 -81
354 1076 17 56 -167
355 1197 19 57 598
356 1187 19 47 -102
357 1158 19 18 560
358 1177 19 37 -168
359 1190 19 50 231
360 1161 19 21 1029
361 1182 19 42 525
362 1212 20 12 237
363 1177 19 37 134
364 1179 19 39 221
365 1097 18 17 -82
366 1183 19 43 -256
367 1076 17 56 -167
368 1177 19 37 -244
369 1195 19 55 118
370 1191 19 51 1090
371 1197 19 57 5
372 1149 19 9 370
373 1171 19 31 1113
374 1184 19 44 1145
375 1177 19 37 38
376 1184 19 44 1005
377 1208 20 8 351
378 1184 19 44 765
379 1188 19 48 172
380 1175 19 35 1001
381 1204 20 4 5
382 1175 19 35 36
383 1179 19 39 -237
384 1190 19 50 -125
385 1167 19 27 -147
386 1181 19 41 790
387 1183 19 43 -111
388 1165 19 25 -134
389 1205 20 5 -173
390 1155 19 15 256
391 1067 17 47 441
392 1154 19 14 199
393 1087 18 7 -292
394 1159 19 19 -100
395 1199 19 59 -183
396 1158 19 18 -92
397 1161 19 21 1102
398 1201 20 1 1143
399 1189 19 49 62
400 1183 19 43 309
401 1118 18 38 874
402 1196 19 56 597
403 1181 19 41 582
404 1156 19 16 517
405 1164 19 24 -220
406 1201 20 1 520
407 1182 19 42 339
408 1168 19 28 -31
409 1158 19 18 -226
410 1190 19 50 -239
411 1090 18 10 526
412 1165 19 25 113
413 1166 19 26 -30
414 1189 19 49 50
415 1163 19 23 1067
416 1161 19 21 -146
417 1216 20 16 437
418 1159 19 19 1100
419 1143 19 3 932
420 1175 19 35 -24
421 1178 19 38 109
422 1148 19 8 370
423 1190 19 50 -249
424 1178 19 38 399
425 1188 19 48 239
426 1196 19 56 -131
427 1226 20 26 1107
428 1198 19 58 419
429 1155 19 15 -163
430 1151 19 11 912
431 1197 19 57 -61
432 1215 20 15 258
433 1165 19 25 238
434 1191 19 51 52
435 1190 19 50 51
436 1109 18 29 1065
437 1178 19 38 121
438 1184 19 44 492
439 1192 19 52 653
440 1108 18 28 -107
441 1183 19 43 -256
442 1213 20 13 140
443 1158 19 18 -268
444 1219 20 19 1057
445 1160 19 20 -278
446 1195 19 55 -147
447 1161 19 21 768
448 1174 19 34 470
449 1185 19 45 -254
450 1167 19 27 -201
451 1175 19 35 -44
452 1172 19 32 944
453 1171 19 31 -252
454 1215 20 15 1033
455 1179 19 39 -140
456 1191 19 51 -246
457 1177 19 37 25
458 1219 20 19 -19
459 1196 19 56 484
460 1226 20 26 705
461 1175 19 35 -126
462 1198 19 58 -1
463 1184 19 44 330
464 1174 19 34 -48
465 1084 18 4 420
466 1152 19 12 301
467 1213 20 13 -49
468 1194 19 54 446
469 1197 19 57 605
470 1217 20 17 1109
471 1160 19 20 -259
472 1189 19 49 -130
473 1162 19 22 325
474 1181 19 41 -73
475 1207 20 7 620
476 1158 19 18 -148
477 1190 19 50 233
478 1156 19 16 395
479 1184 19 44 946
480 1159 19 19 170
481 1133 18 53 -186
482 1200 20 0 121
483 1217 20 17 559
population_density urban suburban midurban rural gymDistanceKm gymIn100m
1 138.590120 FALSE FALSE FALSE TRUE 0.407034 FALSE
2 426.502200 FALSE TRUE TRUE FALSE 0.124418 FALSE
3 1361.000500 TRUE TRUE TRUE FALSE 0.201849 FALSE
4 368.917400 FALSE FALSE TRUE FALSE 0.591283 FALSE
5 1055.898200 TRUE TRUE TRUE FALSE 0.173525 FALSE
6 138.590120 FALSE FALSE FALSE TRUE 0.148218 FALSE
7 1018.295960 TRUE TRUE TRUE FALSE 1.277743 FALSE
8 426.502200 FALSE TRUE TRUE FALSE 0.123943 FALSE
9 2102.977500 TRUE TRUE TRUE FALSE 0.261546 FALSE
10 256.713560 FALSE FALSE TRUE FALSE 0.199990 FALSE
11 1886.212200 TRUE TRUE TRUE FALSE 0.040415 TRUE
12 426.502200 FALSE TRUE TRUE FALSE 0.400353 FALSE
13 475.516240 FALSE TRUE TRUE FALSE 0.213861 FALSE
14 4039.235800 TRUE TRUE TRUE FALSE 0.290158 FALSE
15 493.075470 FALSE TRUE TRUE FALSE 0.210664 FALSE
16 185.232220 FALSE FALSE FALSE TRUE 0.107988 FALSE
17 64.717780 FALSE FALSE FALSE TRUE 0.269349 FALSE
18 2810.728500 TRUE TRUE TRUE FALSE 0.475915 FALSE
19 0.000000 FALSE FALSE FALSE TRUE 0.255312 FALSE
20 10000.000000 TRUE TRUE TRUE FALSE 0.224709 FALSE
21 426.502200 FALSE TRUE TRUE FALSE 0.186745 FALSE
22 1055.898200 TRUE TRUE TRUE FALSE 0.059085 TRUE
23 4039.235800 TRUE TRUE TRUE FALSE 0.016389 TRUE
24 138.590120 FALSE FALSE FALSE TRUE 0.226608 FALSE
25 761.885600 FALSE TRUE TRUE FALSE 0.609028 FALSE
26 1517.408000 TRUE TRUE TRUE FALSE 0.182979 FALSE
27 25.210114 FALSE FALSE FALSE TRUE 0.073484 TRUE
28 3249.458700 TRUE TRUE TRUE FALSE 0.579931 FALSE
29 64.717780 FALSE FALSE FALSE TRUE 0.172263 FALSE
30 683.353900 FALSE TRUE TRUE FALSE 1.167196 FALSE
31 0.000000 FALSE FALSE FALSE TRUE 0.280782 FALSE
32 2431.234100 TRUE TRUE TRUE FALSE 0.082599 TRUE
33 426.502200 FALSE TRUE TRUE FALSE 0.396451 FALSE
34 4039.235800 TRUE TRUE TRUE FALSE 0.257413 FALSE
35 3622.889600 TRUE TRUE TRUE FALSE 0.043225 TRUE
36 1135.319600 TRUE TRUE TRUE FALSE 0.218993 FALSE
37 3022.142600 TRUE TRUE TRUE FALSE 2.553447 FALSE
38 2431.234100 TRUE TRUE TRUE FALSE 0.144393 FALSE
39 2431.234100 TRUE TRUE TRUE FALSE 23.813972 FALSE
40 48.421627 FALSE FALSE FALSE TRUE 0.028319 TRUE
41 426.502200 FALSE TRUE TRUE FALSE 9.864545 FALSE
42 790.019500 FALSE TRUE TRUE FALSE 0.187347 FALSE
43 40.392357 FALSE FALSE FALSE TRUE 3.038946 FALSE
44 0.000000 FALSE FALSE FALSE TRUE 0.283727 FALSE
45 2261.157000 TRUE TRUE TRUE FALSE 0.516173 FALSE
46 382.540280 FALSE FALSE TRUE FALSE 0.727481 FALSE
47 1018.295960 TRUE TRUE TRUE FALSE 0.212025 FALSE
48 5597.982000 TRUE TRUE TRUE FALSE 0.440074 FALSE
49 1955.863600 TRUE TRUE TRUE FALSE 0.211762 FALSE
50 55.979820 FALSE FALSE FALSE TRUE 0.888201 FALSE
51 8044.736300 TRUE TRUE TRUE FALSE 0.236166 FALSE
52 1411.257600 TRUE TRUE TRUE FALSE 0.254033 FALSE
53 382.540280 FALSE FALSE TRUE FALSE 3.292003 FALSE
54 119.878180 FALSE FALSE FALSE TRUE 0.270268 FALSE
55 947.061000 TRUE TRUE TRUE FALSE 0.341335 FALSE
56 880.809300 TRUE TRUE TRUE FALSE 0.386664 FALSE
57 2102.977500 TRUE TRUE TRUE FALSE 0.183578 FALSE
58 426.502200 FALSE TRUE TRUE FALSE 0.109560 FALSE
59 230.252700 FALSE FALSE TRUE FALSE 4.300162 FALSE
60 3622.889600 TRUE TRUE TRUE FALSE 0.160779 FALSE
61 6019.044400 TRUE TRUE TRUE FALSE 0.383060 FALSE
62 5597.982000 TRUE TRUE TRUE FALSE 0.184328 FALSE
63 1573.440800 TRUE TRUE TRUE FALSE 0.759833 FALSE
64 0.000000 FALSE FALSE FALSE TRUE 0.386547 FALSE
65 1135.319600 TRUE TRUE TRUE FALSE 4.653035 FALSE
66 296.784300 FALSE FALSE TRUE FALSE 1.219406 FALSE
67 1055.898200 TRUE TRUE TRUE FALSE 0.770183 FALSE
68 40.392357 FALSE FALSE FALSE TRUE 2.931848 FALSE
69 67.107590 FALSE FALSE FALSE TRUE 0.890209 FALSE
70 761.885600 FALSE TRUE TRUE FALSE 2.795403 FALSE
71 58.046967 FALSE FALSE FALSE TRUE 0.309267 FALSE
72 1018.295960 TRUE TRUE TRUE FALSE 0.480806 FALSE
73 192.072220 FALSE FALSE FALSE TRUE 0.294766 FALSE
74 256.713560 FALSE FALSE TRUE FALSE 0.260981 FALSE
75 1135.319600 TRUE TRUE TRUE FALSE 1.231092 FALSE
76 396.666200 FALSE FALSE TRUE FALSE 0.265291 FALSE
77 683.353900 FALSE TRUE TRUE FALSE 0.121772 FALSE
78 2261.157000 TRUE TRUE TRUE FALSE 0.042667 TRUE
79 426.502200 FALSE TRUE TRUE FALSE 20.179808 FALSE
80 25.210114 FALSE FALSE FALSE TRUE 0.465010 FALSE
81 570.040200 FALSE TRUE TRUE FALSE 0.562783 FALSE
82 43.430546 FALSE FALSE FALSE TRUE 0.048752 TRUE
83 0.000000 FALSE FALSE FALSE TRUE 0.103804 FALSE
84 3133.740200 TRUE TRUE TRUE FALSE 0.186950 FALSE
85 222.053020 FALSE FALSE TRUE FALSE 0.073466 TRUE
86 40.392357 FALSE FALSE FALSE TRUE 2.920324 FALSE
87 3622.889600 TRUE TRUE TRUE FALSE 0.238376 FALSE
88 266.193150 FALSE FALSE TRUE FALSE 0.344684 FALSE
89 0.000000 FALSE FALSE FALSE TRUE 0.281706 FALSE
90 296.784300 FALSE FALSE TRUE FALSE 0.284722 FALSE
91 3249.458700 TRUE TRUE TRUE FALSE 0.173890 FALSE
92 256.713560 FALSE FALSE TRUE FALSE 0.320550 FALSE
93 2431.234100 TRUE TRUE TRUE FALSE 25.047054 FALSE
94 1631.542600 TRUE TRUE TRUE FALSE 0.168634 FALSE
95 256.713560 FALSE FALSE TRUE FALSE 0.209474 FALSE
96 38.953922 FALSE FALSE FALSE TRUE 3.509740 FALSE
97 1361.000500 TRUE TRUE TRUE FALSE 1.208641 FALSE
98 2261.157000 TRUE TRUE TRUE FALSE 0.079219 TRUE
99 1055.898200 TRUE TRUE TRUE FALSE 0.234715 FALSE
100 761.885600 FALSE TRUE TRUE FALSE 0.908098 FALSE
101 3249.458700 TRUE TRUE TRUE FALSE 0.751233 FALSE
102 214.145360 FALSE FALSE TRUE FALSE 0.469080 FALSE
103 761.885600 FALSE TRUE TRUE FALSE 0.218146 FALSE
104 442.251460 FALSE TRUE TRUE FALSE 0.146082 FALSE
105 247.571580 FALSE FALSE TRUE FALSE 0.253302 FALSE
106 1055.898200 TRUE TRUE TRUE FALSE 0.444015 FALSE
107 3133.740200 TRUE TRUE TRUE FALSE 0.042110 TRUE
108 38.953922 FALSE FALSE FALSE TRUE 0.329159 FALSE
109 2710.633800 TRUE TRUE TRUE FALSE 0.557522 FALSE
110 5206.375000 TRUE TRUE TRUE FALSE 0.243227 FALSE
111 382.540280 FALSE FALSE TRUE FALSE 3.564925 FALSE
112 570.040200 FALSE TRUE TRUE FALSE 0.031867 TRUE
113 330.891050 FALSE FALSE TRUE FALSE 0.377676 FALSE
114 103.692665 FALSE FALSE FALSE TRUE 0.137674 FALSE
115 2261.157000 TRUE TRUE TRUE FALSE 0.242436 FALSE
116 3622.889600 TRUE TRUE TRUE FALSE 0.234734 FALSE
117 0.000000 FALSE FALSE FALSE TRUE 0.285020 FALSE
118 172.274290 FALSE FALSE FALSE TRUE 0.140410 FALSE
119 982.032800 TRUE TRUE TRUE FALSE 0.377823 FALSE
120 761.885600 FALSE TRUE TRUE FALSE 6.630430 FALSE
121 761.885600 FALSE TRUE TRUE FALSE 0.630630 FALSE
122 913.334600 TRUE TRUE TRUE FALSE 0.193617 FALSE
123 62.413080 FALSE FALSE FALSE TRUE 0.088911 TRUE
124 382.540280 FALSE FALSE TRUE FALSE 0.732584 FALSE
125 3895.392000 TRUE TRUE TRUE FALSE 0.204509 FALSE
126 2344.653800 TRUE TRUE TRUE FALSE 0.066011 TRUE
127 982.032800 TRUE TRUE TRUE FALSE 0.340885 FALSE
128 6471.778300 TRUE TRUE TRUE FALSE 0.218392 FALSE
129 1135.319600 TRUE TRUE TRUE FALSE 0.223294 FALSE
130 947.061000 TRUE TRUE TRUE FALSE 0.146818 FALSE
131 5020.967300 TRUE TRUE TRUE FALSE 0.418697 FALSE
132 426.502200 FALSE TRUE TRUE FALSE 0.428870 FALSE
133 107.521680 FALSE FALSE FALSE TRUE 0.533255 FALSE
134 1135.319600 TRUE TRUE TRUE FALSE 4.430470 FALSE
135 790.019500 FALSE TRUE TRUE FALSE 0.322604 FALSE
136 2431.234100 TRUE TRUE TRUE FALSE 55.900999 FALSE
137 913.334600 TRUE TRUE TRUE FALSE 0.619898 FALSE
138 143.707800 FALSE FALSE FALSE TRUE 0.736078 FALSE
139 3622.889600 TRUE TRUE TRUE FALSE 0.332617 FALSE
140 1517.408000 TRUE TRUE TRUE FALSE 0.232726 FALSE
141 1055.898200 TRUE TRUE TRUE FALSE 0.135391 FALSE
142 4188.391000 TRUE TRUE TRUE FALSE 0.234844 FALSE
143 25.210114 FALSE FALSE FALSE TRUE 0.311569 FALSE
144 2102.977500 TRUE TRUE TRUE FALSE 0.215700 FALSE
145 849.442260 TRUE TRUE TRUE FALSE 0.202599 FALSE
146 34.938730 FALSE FALSE FALSE TRUE 0.413479 FALSE
147 1886.212200 TRUE TRUE TRUE FALSE 0.119382 FALSE
148 2261.157000 TRUE TRUE TRUE FALSE 0.378442 FALSE
149 4188.391000 TRUE TRUE TRUE FALSE 0.194631 FALSE
150 247.571580 FALSE FALSE TRUE FALSE 0.223994 FALSE
151 0.000000 FALSE FALSE FALSE TRUE 0.153117 FALSE
152 4669.725600 TRUE TRUE TRUE FALSE 0.804651 FALSE
153 2810.728500 TRUE TRUE TRUE FALSE 0.270832 FALSE
154 2810.728500 TRUE TRUE TRUE FALSE 0.734848 FALSE
155 913.334600 TRUE TRUE TRUE FALSE 0.122982 FALSE
156 2431.234100 TRUE TRUE TRUE FALSE 0.141405 FALSE
157 62.413080 FALSE FALSE FALSE TRUE 0.164146 FALSE
158 256.713560 FALSE FALSE TRUE FALSE 0.444878 FALSE
159 2431.234100 TRUE TRUE TRUE FALSE 0.331310 FALSE
160 368.917400 FALSE FALSE TRUE FALSE 0.198089 FALSE
161 411.313750 FALSE TRUE TRUE FALSE 0.184750 FALSE
162 199.164810 FALSE FALSE FALSE TRUE 0.160276 FALSE
163 2261.157000 TRUE TRUE TRUE FALSE 0.098322 TRUE
164 761.885600 FALSE TRUE TRUE FALSE 0.317952 FALSE
165 6241.307600 TRUE TRUE TRUE FALSE 0.225758 FALSE
166 4669.725600 TRUE TRUE TRUE FALSE 0.280699 FALSE
167 1094.888900 TRUE TRUE TRUE FALSE 5.048011 FALSE
168 761.885600 FALSE TRUE TRUE FALSE 0.869953 FALSE
169 1573.440800 TRUE TRUE TRUE FALSE 0.050139 TRUE
170 458.582340 FALSE TRUE TRUE FALSE 0.221099 FALSE
171 1055.898200 TRUE TRUE TRUE FALSE 0.604699 FALSE
172 849.442260 TRUE TRUE TRUE FALSE 0.463192 FALSE
173 160.222840 FALSE FALSE FALSE TRUE 1.824068 FALSE
174 138.590120 FALSE FALSE FALSE TRUE 2.339596 FALSE
175 734.753600 FALSE TRUE TRUE FALSE 0.304428 FALSE
176 1955.863600 TRUE TRUE TRUE FALSE 0.453822 FALSE
177 442.251460 FALSE TRUE TRUE FALSE 0.317316 FALSE
178 4669.725600 TRUE TRUE TRUE FALSE 0.393980 FALSE
179 635.549800 FALSE TRUE TRUE FALSE 0.928867 FALSE
180 2102.977500 TRUE TRUE TRUE FALSE 0.117494 FALSE
181 426.502200 FALSE TRUE TRUE FALSE 0.319524 FALSE
182 761.885600 FALSE TRUE TRUE FALSE 0.401356 FALSE
183 1018.295960 TRUE TRUE TRUE FALSE 0.568120 FALSE
184 296.784300 FALSE FALSE TRUE FALSE 3.095946 FALSE
185 0.000000 FALSE FALSE FALSE TRUE 0.305654 FALSE
186 4188.391000 TRUE TRUE TRUE FALSE 0.084360 TRUE
187 570.040200 FALSE TRUE TRUE FALSE 0.560569 FALSE
188 93.004490 FALSE FALSE FALSE TRUE 0.391458 FALSE
189 0.000000 FALSE FALSE FALSE TRUE 0.365300 FALSE
190 86.498360 FALSE FALSE FALSE TRUE 2.244386 FALSE
191 50.209675 FALSE FALSE FALSE TRUE 0.059467 TRUE
192 0.000000 FALSE FALSE FALSE TRUE 0.375059 FALSE
193 9643.884000 TRUE TRUE TRUE FALSE 0.249883 FALSE
194 2261.157000 TRUE TRUE TRUE FALSE 0.507387 FALSE
195 18.862122 FALSE FALSE FALSE TRUE 0.328277 FALSE
196 426.502200 FALSE TRUE TRUE FALSE 0.235427 FALSE
197 947.061000 TRUE TRUE TRUE FALSE 3.641917 FALSE
198 115.609130 FALSE FALSE FALSE TRUE 0.883897 FALSE
199 947.061000 TRUE TRUE TRUE FALSE 0.656379 FALSE
200 296.784300 FALSE FALSE TRUE FALSE 0.210202 FALSE
201 18.862122 FALSE FALSE FALSE TRUE 2.439506 FALSE
202 1411.257600 TRUE TRUE TRUE FALSE 0.361526 FALSE
203 2431.234100 TRUE TRUE TRUE FALSE 0.109411 FALSE
204 9643.884000 TRUE TRUE TRUE FALSE 0.143732 FALSE
205 475.516240 FALSE TRUE TRUE FALSE 0.516899 FALSE
206 138.590120 FALSE FALSE FALSE TRUE 0.194846 FALSE
207 1055.898200 TRUE TRUE TRUE FALSE 1.773556 FALSE
208 1955.863600 TRUE TRUE TRUE FALSE 0.211762 FALSE
209 5804.697000 TRUE TRUE TRUE FALSE 0.332775 FALSE
210 4039.235800 TRUE TRUE TRUE FALSE 0.474922 FALSE
211 107.521680 FALSE FALSE FALSE TRUE 0.072188 TRUE
212 1055.898200 TRUE TRUE TRUE FALSE 0.175332 FALSE
213 1886.212200 TRUE TRUE TRUE FALSE 0.032295 TRUE
214 2102.977500 TRUE TRUE TRUE FALSE 0.034744 TRUE
215 160.222840 FALSE FALSE FALSE TRUE 0.076048 TRUE
216 107.521680 FALSE FALSE FALSE TRUE 0.333145 FALSE
217 0.000000 FALSE FALSE FALSE TRUE 0.178904 FALSE
218 1631.542600 TRUE TRUE TRUE FALSE 0.025760 TRUE
219 426.502200 FALSE TRUE TRUE FALSE 0.372743 FALSE
220 0.000000 FALSE FALSE FALSE TRUE 0.114337 FALSE
221 93.004490 FALSE FALSE FALSE TRUE 0.857799 FALSE
222 761.885600 FALSE TRUE TRUE FALSE 0.926941 FALSE
223 178.635800 FALSE FALSE FALSE TRUE 1.245333 FALSE
224 570.040200 FALSE TRUE TRUE FALSE 0.126828 FALSE
225 493.075470 FALSE TRUE TRUE FALSE 0.175521 FALSE
226 659.018550 FALSE TRUE TRUE FALSE 0.118337 FALSE
227 149.014430 FALSE FALSE FALSE TRUE 3.066171 FALSE
228 1055.898200 TRUE TRUE TRUE FALSE 0.091868 TRUE
229 25.210114 FALSE FALSE FALSE TRUE 0.744243 FALSE
230 330.891050 FALSE FALSE TRUE FALSE 0.243231 FALSE
231 256.713560 FALSE FALSE TRUE FALSE 0.215790 FALSE
232 111.492096 FALSE FALSE FALSE TRUE 0.247689 FALSE
233 38.953922 FALSE FALSE FALSE TRUE 0.032325 TRUE
234 1135.319600 TRUE TRUE TRUE FALSE 4.427568 FALSE
235 426.502200 FALSE TRUE TRUE FALSE 1.248560 FALSE
236 849.442260 TRUE TRUE TRUE FALSE 1.083551 FALSE
237 458.582340 FALSE TRUE TRUE FALSE 3.340507 FALSE
238 475.516240 FALSE TRUE TRUE FALSE 4.632544 FALSE
239 849.442260 TRUE TRUE TRUE FALSE 0.154202 FALSE
240 34.938730 FALSE FALSE FALSE TRUE 0.443516 FALSE
241 25.210114 FALSE FALSE FALSE TRUE 0.440614 FALSE
242 880.809300 TRUE TRUE TRUE FALSE 0.117054 FALSE
243 86.498360 FALSE FALSE FALSE TRUE 0.218878 FALSE
244 475.516240 FALSE TRUE TRUE FALSE 1.329609 FALSE
245 41.883910 FALSE FALSE FALSE TRUE 0.165470 FALSE
246 89.692450 FALSE FALSE FALSE TRUE 0.074659 TRUE
247 266.193150 FALSE FALSE TRUE FALSE 0.067985 TRUE
248 475.516240 FALSE TRUE TRUE FALSE 3.011186 FALSE
249 947.061000 TRUE TRUE TRUE FALSE 0.371013 FALSE
250 4842.162600 TRUE TRUE TRUE FALSE 0.108440 FALSE
251 1463.370600 TRUE TRUE TRUE FALSE 0.247316 FALSE
252 1955.863600 TRUE TRUE TRUE FALSE 0.100105 FALSE
253 1177.243000 TRUE TRUE TRUE FALSE 0.116220 FALSE
254 9643.884000 TRUE TRUE TRUE FALSE 0.503627 FALSE
255 426.502200 FALSE TRUE TRUE FALSE 9.614691 FALSE
256 1055.898200 TRUE TRUE TRUE FALSE 0.073041 TRUE
257 913.334600 TRUE TRUE TRUE FALSE 0.346656 FALSE
258 2431.234100 TRUE TRUE TRUE FALSE 0.245037 FALSE
259 25.210114 FALSE FALSE FALSE TRUE 0.427905 FALSE
260 192.072220 FALSE FALSE FALSE TRUE 0.298536 FALSE
261 426.502200 FALSE TRUE TRUE FALSE 9.924856 FALSE
262 411.313750 FALSE TRUE TRUE FALSE 0.297016 FALSE
263 111.492096 FALSE FALSE FALSE TRUE 0.619545 FALSE
264 612.916900 FALSE TRUE TRUE FALSE 6.180030 FALSE
265 96.438835 FALSE FALSE FALSE TRUE 1.107835 FALSE
266 1955.863600 TRUE TRUE TRUE FALSE 0.637515 FALSE
267 43.430546 FALSE FALSE FALSE TRUE 0.194496 FALSE
268 6241.307600 TRUE TRUE TRUE FALSE 0.722174 FALSE
269 247.571580 FALSE FALSE TRUE FALSE 1.543890 FALSE
270 7758.250000 TRUE TRUE TRUE FALSE 0.084639 TRUE
271 1463.370600 TRUE TRUE TRUE FALSE 0.703247 FALSE
272 256.713560 FALSE FALSE TRUE FALSE 0.145702 FALSE
273 683.353900 FALSE TRUE TRUE FALSE 0.295201 FALSE
274 2521.011500 TRUE TRUE TRUE FALSE 0.448977 FALSE
275 10000.000000 TRUE TRUE TRUE FALSE 0.229658 FALSE
276 849.442260 TRUE TRUE TRUE FALSE 0.447835 FALSE
277 2521.011500 TRUE TRUE TRUE FALSE 0.213701 FALSE
278 1361.000500 TRUE TRUE TRUE FALSE 0.443285 FALSE
279 1018.295960 TRUE TRUE TRUE FALSE 0.457610 FALSE
280 683.353900 FALSE TRUE TRUE FALSE 0.399369 FALSE
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282 38.953922 FALSE FALSE FALSE TRUE 0.506044 FALSE
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285 1754.262100 TRUE TRUE TRUE FALSE 1.386332 FALSE
286 761.885600 FALSE TRUE TRUE FALSE 0.163485 FALSE
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288 458.582340 FALSE TRUE TRUE FALSE 1.026229 FALSE
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290 0.000000 FALSE FALSE FALSE TRUE 0.682496 FALSE
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294 256.713560 FALSE FALSE TRUE FALSE 0.127469 FALSE
295 185.232220 FALSE FALSE FALSE TRUE 0.119864 FALSE
296 1517.408000 TRUE TRUE TRUE FALSE 0.012170 TRUE
297 50.209675 FALSE FALSE FALSE TRUE 0.336237 FALSE
298 0.000000 FALSE FALSE FALSE TRUE 0.043826 TRUE
299 659.018550 FALSE TRUE TRUE FALSE 0.252958 FALSE
300 426.502200 FALSE TRUE TRUE FALSE 0.340714 FALSE
301 25.210114 FALSE FALSE FALSE TRUE 4.419181 FALSE
302 1055.898200 TRUE TRUE TRUE FALSE 0.154187 FALSE
303 1312.533000 TRUE TRUE TRUE FALSE 0.125821 FALSE
304 21.806335 FALSE FALSE FALSE TRUE 15.261386 FALSE
305 0.000000 FALSE FALSE FALSE TRUE 0.202368 FALSE
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307 111.492096 FALSE FALSE FALSE TRUE 0.226978 FALSE
308 3622.889600 TRUE TRUE TRUE FALSE 0.144807 FALSE
309 1094.888900 TRUE TRUE TRUE FALSE 0.113829 FALSE
310 368.917400 FALSE FALSE TRUE FALSE 0.505645 FALSE
311 475.516240 FALSE TRUE TRUE FALSE 0.037504 TRUE
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313 475.516240 FALSE TRUE TRUE FALSE 0.623611 FALSE
314 0.000000 FALSE FALSE FALSE TRUE 3.695032 FALSE
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316 40.392357 FALSE FALSE FALSE TRUE 3.176434 FALSE
317 1177.243000 TRUE TRUE TRUE FALSE 22.880993 FALSE
318 1754.262100 TRUE TRUE TRUE FALSE 0.842291 FALSE
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338 3622.889600 TRUE TRUE TRUE FALSE 0.011200 TRUE
339 10000.000000 TRUE TRUE TRUE FALSE 0.152901 FALSE
340 570.040200 FALSE TRUE TRUE FALSE 0.075295 TRUE
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375 149.014430 FALSE FALSE FALSE TRUE 3.025374 FALSE
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402 0.000000 FALSE FALSE FALSE TRUE 0.122806 FALSE
403 256.713560 FALSE FALSE TRUE FALSE 0.418505 FALSE
404 982.032800 TRUE TRUE TRUE FALSE 0.650602 FALSE
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pokestopIn2500m pokestopIn5000m cooc_1 cooc_2 cooc_3 cooc_4 cooc_5 cooc_6
1 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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7 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
8 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
9 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
10 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
11 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
12 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
13 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
14 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
15 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
16 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
17 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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21 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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24 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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429 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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441 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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444 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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449 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
450 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
451 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
452 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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457 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
458 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
459 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
460 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
461 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
462 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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465 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
466 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
467 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
468 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
469 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
470 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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472 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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481 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
482 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
483 TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
cooc_7 cooc_8 cooc_9 cooc_10 cooc_11 cooc_12 cooc_13 cooc_14 cooc_15
1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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cooc_16 cooc_17 cooc_18 cooc_19 cooc_20 cooc_21 cooc_22 cooc_23 cooc_24
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cooc_43 cooc_44 cooc_45 cooc_46 cooc_47 cooc_48 cooc_49 cooc_50 cooc_51
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cooc_70 cooc_71 cooc_72 cooc_73 cooc_74 cooc_75 cooc_76 cooc_77 cooc_78
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4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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cooc_145 cooc_146 cooc_147 cooc_148 cooc_149 cooc_150 cooc_151 class
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415 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 69
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418 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 10
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420 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 98
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422 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 56
423 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 41
424 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 16
425 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 133
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430 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 116
431 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 13
432 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 92
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435 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 21
436 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 63
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438 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 10
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443 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 79
444 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 29
445 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 98
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447 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 41
448 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 96
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450 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 16
451 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 48
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454 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 96
455 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 79
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458 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 35
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460 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 16
461 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 16
462 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 1
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464 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 19
465 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 10
466 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 133
467 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 41
468 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 46
469 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 16
470 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 129
471 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 120
472 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 17
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477 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 19
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481 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 140
482 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 13
483 FALSE FALSE FALSE FALSE FALSE FALSE FALSE 48
[ reached 'max' / getOption("max.print") -- omitted 29119 rows ]
Reading Vectors: Point data
Now we use another common GIS package,sp, to convert this table into a point shapefile.
library(sp)
xy <- tibble(longitude = pkmng_points_dat$longitude,
latitude = pkmng_points_dat$latitude)
pkmng_points <- SpatialPointsDataFrame(coords = xy,
data = pkmng_points_dat)
Reading Vectors: Point data
pkmng_points
class : SpatialPointsDataFrame
features : 29602
extent : -158.0198, 175.6162, -42.9845, 68.43698 (xmin, xmax, ymin, ymax)
variables : 207
# A tibble: 29,602 x 207
pokemonId latitude longitude appearedLocalTi… cellId_90m cellId_180m
<int> <dbl> <dbl> <fct> <dbl> <dbl>
1 16 53.7 -0.443 2016-09-04T09:3… 5.22e18 5.22e18
2 60 46.0 8.96 2016-09-06T07:5… 5.15e18 5.15e18
3 19 46.2 15.3 2016-09-04T08:3… 5.14e18 5.14e18
4 16 50.7 14.5 2016-09-04T09:2… 5.12e18 5.12e18
5 23 44.5 11.3 2016-09-04T16:0… 5.15e18 5.15e18
6 120 50.2 14.7 2016-09-03T15:5… 5.12e18 5.12e18
7 46 33.6 -84.3 2016-09-07T01:0… 9.87e18 9.87e18
8 10 46.0 8.95 2016-09-06T06:2… 5.15e18 5.15e18
9 27 33.9 -118. 2016-09-06T05:1… 9.28e18 9.28e18
10 23 34.3 -119. 2016-09-06T14:4… 9.29e18 9.29e18
# … with 29,592 more rows, and 201 more variables: cellId_370m <dbl>,
# cellId_730m <dbl>, cellId_1460m <dbl>, cellId_2920m <dbl>,
# cellId_5850m <dbl>, appearedTimeOfDay <fct>, appearedHour <int>,
# appearedMinute <int>, appearedDayOfWeek <fct>, appearedDay <int>,
# appearedMonth <int>, appearedYear <int>, terrainType <int>,
# closeToWater <lgl>, city <fct>, continent <fct>, weather <fct>,
# temperature <dbl>, windSpeed <dbl>, windBearing <int>, pressure <dbl>,
# weatherIcon <fct>, sunriseMinutesMidnight <int>, sunriseHour <int>,
# sunriseMinute <int>, sunriseMinutesSince <int>,
# sunsetMinutesMidnight <int>, sunsetHour <int>, sunsetMinute <int>,
# sunsetMinutesBefore <int>, population_density <dbl>, urban <lgl>,
# suburban <lgl>, midurban <lgl>, rural <lgl>, gymDistanceKm <dbl>,
# gymIn100m <lgl>, gymIn250m <lgl>, gymIn500m <lgl>, gymIn1000m <lgl>,
# gymIn2500m <lgl>, gymIn5000m <lgl>, pokestopDistanceKm <dbl>,
# pokestopIn100m <lgl>, pokestopIn250m <lgl>, pokestopIn500m <lgl>,
# pokestopIn1000m <lgl>, pokestopIn2500m <lgl>, pokestopIn5000m <lgl>,
# cooc_1 <lgl>, cooc_2 <lgl>, cooc_3 <lgl>, cooc_4 <lgl>, cooc_5 <lgl>,
# cooc_6 <lgl>, cooc_7 <lgl>, cooc_8 <lgl>, cooc_9 <lgl>, cooc_10 <lgl>,
# cooc_11 <lgl>, cooc_12 <lgl>, cooc_13 <lgl>, cooc_14 <lgl>, cooc_15 <lgl>,
# cooc_16 <lgl>, cooc_17 <lgl>, cooc_18 <lgl>, cooc_19 <lgl>, cooc_20 <lgl>,
# cooc_21 <lgl>, cooc_22 <lgl>, cooc_23 <lgl>, cooc_24 <lgl>, cooc_25 <lgl>,
# cooc_26 <lgl>, cooc_27 <lgl>, cooc_28 <lgl>, cooc_29 <lgl>, cooc_30 <lgl>,
# cooc_31 <lgl>, cooc_32 <lgl>, cooc_33 <lgl>, cooc_34 <lgl>, cooc_35 <lgl>,
# cooc_36 <lgl>, cooc_37 <lgl>, cooc_38 <lgl>, cooc_39 <lgl>, cooc_40 <lgl>,
# cooc_41 <lgl>, cooc_42 <lgl>, cooc_43 <lgl>, cooc_44 <lgl>, cooc_45 <lgl>,
# cooc_46 <lgl>, cooc_47 <lgl>, cooc_48 <lgl>, cooc_49 <lgl>, cooc_50 <lgl>,
# cooc_51 <lgl>, …
Reading Rasters
Finally, importing rasters is easily done with the raster package.
library(raster)
pkmng_raster <- raster("Data/pkmng_raster.tif")
Reading Rasters
Parts of a raster object
pkmng_raster
class : RasterLayer
dimensions : 87, 180, 15660 (nrow, ncol, ncell)
resolution : 2, 2 (x, y)
extent : -180, 180, -90.42973, 83.57027 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : /Users/Hannah/Dropbox/RWorkingGroup/SpatialAnalysis1/Data/pkmng_raster.tif
names : pkmng_raster
values : 0, 9.535896 (min, max)
Basic visualization
Just use the plot() function!
plot(pkmng_countries, col = "yellow")
Basic visualization
Just use the plot() function!
plot(pkmng_points)
Basic visualization
You can stack map layers, too.
plot(pkmng_countries, col = "yellow")
points(pkmng_points, pch = 20, cex = .7, col = "red")
Basic visualization
Rasters also use the plot() function!
plot(pkmng_raster)
Basic visualization
You can stack multiple plots that mix rasters and vectors using add argument.
plot(pkmng_raster, col = viridis::viridis(n = 10))
plot(pkmng_countries, add = T)
Challenge 1: Load and display spatial data
Now it's your turn! Load the shapefile, point csv, and raster and plot them all together. Data can be found in the “Data” folder in the SpatialAnalysis project on GitHub.
Solution 1: Load and display spatial data
Now it's your turn! Load the shapefile, point csv, and raster and plot them all together. Data can be found in the “Data” folder in the SpatialAnalysis project on GitHub.
Projections, Extents, and Units
Projections
One of the most important things to remember when dealing with spatial data!!
A projection is a mathematical transformation of coordinates from the surface of a sphere on a 2D plane.
Types of projections
By surface:
- Cylindrical
- Conic
- Azimuthal
By preservation of a metric property
- Conformal (preserves shape, distorts area)
- Equal-area (preserves area, distorts shape)
- Equidistant (preserves distance from some point or line)
Checking projection of your spatial objects
You can view the Coordinate Reference System of your spatial objects:
crs(pkmng_countries)
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
crs(pkmng_raster)
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
Checking projection of spatial objects
But our points shapefile has no projection, because we didn't set it when creating the object.
crs(pkmng_points)
CRS arguments: NA
Setting projection of spatial objects
Two options for defining projections:
- Writing a string of desired projection
- Extract projection from existing object
proj1 <- crs("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
proj2 <- crs(pkmng_countries)
proj2
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
Setting projection of spatial objects
Once you've defined the projection, add it to your object
proj4string(pkmng_points) <- proj1
pkmng_points
class : SpatialPointsDataFrame
features : 29602
extent : -158.0198, 175.6162, -42.9845, 68.43698 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
variables : 207
names : pokemonId, latitude, longitude, appearedLocalTime, cellId_90m, cellId_180m, cellId_370m, cellId_730m, cellId_1460m, cellId_2920m, cellId_5850m, appearedTimeOfDay, appearedHour, appearedMinute, appearedDayOfWeek, ...
min values : 1, -42.984504, -158.019758, 2016-09-02T21:50:11Z, 43172735374852096, 43172735576178688, 43172735307743232, 43172732086517760, 43172719201615872, 43172667662008320, 43172598942531584, afternoon, 0, 0, dummy_day, ...
max values : 149, 68.43698, 175.616223, 2016-09-08T03:51:27Z, 12280882455157669888, 12280882455224778752, 12280882455493214208, 1.2280882456567e+19, 12280882443682054144, 12280882495221661696, 12280882701380091904, night, 23, 59, Wednesday, ...
Transform projection of spatial objects
When you want to change the projection of a spatial object
pkmng_points_p <- spTransform(pkmng_points,
crs("+proj=laea +lat_0=0 +lon_0=0"))
pkmng_points_p
class : SpatialPointsDataFrame
features : 29602
extent : -9285067, 11178209, -11993372, 9520128 (xmin, xmax, ymin, ymax)
crs : +proj=laea +lat_0=0 +lon_0=0 +ellps=WGS84
variables : 207
names : pokemonId, latitude, longitude, appearedLocalTime, cellId_90m, cellId_180m, cellId_370m, cellId_730m, cellId_1460m, cellId_2920m, cellId_5850m, appearedTimeOfDay, appearedHour, appearedMinute, appearedDayOfWeek, ...
min values : 1, -42.984504, -158.019758, 2016-09-02T21:50:11Z, 43172735374852096, 43172735576178688, 43172735307743232, 43172732086517760, 43172719201615872, 43172667662008320, 43172598942531584, afternoon, 0, 0, dummy_day, ...
max values : 149, 68.43698, 175.616223, 2016-09-08T03:51:27Z, 12280882455157669888, 12280882455224778752, 12280882455493214208, 1.2280882456567e+19, 12280882443682054144, 12280882495221661696, 12280882701380091904, night, 23, 59, Wednesday, ...
Why are projections so important?
Here's our points mapped on top of a shapefile of countries:
plot(pkmng_countries, axes = T)
plot(pkmng_points, add = T, pch = 1, col = "red")
Why are projections so important?
Projections don't match:
countries_p <- spTransform(pkmng_countries,
crs("+proj=laea +lat_0=0 +lon_0=0"))
plot(countries_p, axes = T)
plot(pkmng_points, add = T, pch = 1, col = "red")
Projections do match:
plot(countries_p, axes = T)
plot(pkmng_points_p, add = T, pch = 1, col = "red")
Extent: Vectors
- Minimum and maximum X and Y coordinates of the object
- Depends on projections for units
pkmng_countries@bbox
min max
x -180 180.00000
y -90 83.57027
countries_p@bbox
min max
x -12726751 12733710
y -12641498 12299760
Extent: Rasters
For raster objects, you find the extent with this syntax:
pkmng_raster@extent
class : Extent
xmin : -180
xmax : 180
ymin : -90.42973
ymax : 83.57027
Changing raster extents
- Reduce extent using
crop() - Increase extent using
extend()
pkmng_raster_c <- crop(pkmng_raster,
extent(-50, 100, 0, 80))
Changing raster extents
All the red cells are in the new, limited extent!
plot(pkmng_raster)
plot(pkmng_raster_c, add = T, col = "red")
Why is extent important?
Mismatching extents and projections result in errors
raster::intersect(countries_p, pkmng_raster_c)
NULL
Challenge 2: Load and display spatial data
Now it's your turn! Try cropping the pkmng_raster to the Northern Hemisphere and then projecting it using “+proj=laea +lat_0=45 +lon_0=0”. Bonus if you reproject and plot the country spatial file on top of it.
Solution 2: Load and display spatial data
pkmnh_rast_crop <- crop(pkmng_raster, extent(c(-180, 180, 0, 90)))
pkmng_raster_crop_rprj <- projectRaster(pkmnh_rast_crop,
crs = "+proj=laea +lat_0=45 +lon_0=0")
plot(pkmng_raster_crop_rprj)
plot(spTransform(pkmng_countries,
CRSobj = "+proj=laea +lat_0=45 +lon_0=0"), add = T)
Some Basic Spatial Manipulation and Analysis
Geoprocessing
- GIS operations used to manipulate spatial data
- Input dataset -> operation -> output dataset
Geoprocessing: Buffer points
- creates a polygon based on proximity
Example: buffer some pokemon points to 100km radius
We'll first subset the data
sub_pkmng_p<- subset(pkmng_points, pokemonId %in% c(8:10))
plot(pkmng_countries)
plot(sub_pkmng_p,add = T, col = "red")
Geoprocessing: Buffer points
Now we buffer with 100km radius
buffer_pkmng_points<- raster::buffer(sub_pkmng_p,width = 100000)
plot(pkmng_countries)
plot(buffer_pkmng_points,add = T, col = "red")
Geoprocessing: Clip
- Overlay function that cuts out an input layer with extent of a defined feature boundary
- Result of this tool is a new clipped output layer.
Example: clip buffered points to ones in Italy, and to fit boundaries of Italian coastline.
Geoprocessing: Clip
First, filter Italy out of the countries shapefile
Italy <- pkmng_countries %>% filter(NAME == "Italy")
plot(Italy)
plot(buffer_pkmng_points, add = T, col = "red")
Geoprocessing: Clip
Now clip buffered points based on Italian shapefile we created
clip_pkmng_rgeos<- rgeos::gIntersection(Italy, buffer_pkmng_points,
byid = TRUE, drop_lower_td = TRUE)
plot(Italy)
plot(clip_pkmng_rgeos, add = T, col = "red")
Geoprocessing: Intersect
- similar to clip tool because the extent of input features defines the output
- BUT it preserves attributes from all data sets that overlap each other in the output
Lets intersect the points with the country shapefile to find which points belong to which country
intersect_pkmg<- raster::intersect(pkmng_points,pkmng_countries)
Geoprocessing: Intersect
head(pkmng_countries@data)[,5:7]
NAME AREA POP2005
0 Antigua and Barbuda 44 83039
1 Algeria 238174 32854159
2 Azerbaijan 8260 8352021
3 Albania 2740 3153731
4 Armenia 2820 3017661
5 Angola 124670 16095214
head(intersect_pkmg@data)[,c(1:3,212:214)]
pokemonId latitude longitude NAME AREA POP2005
1 16 53.68574 -0.443450 United Kingdom 24193 60244834
2 60 46.00403 8.957153 Switzerland 4000 7424389
3 19 46.23540 15.254241 Slovenia 2014 1999425
4 16 50.67543 14.541165 Czech Republic 7727 10191762
5 23 44.49420 11.344701 Italy 29411 5864636
6 120 50.18774 14.657816 Czech Republic 7727 10191762
Geoprocessing: Merge
- Combines datasets that are the same data type
#subset the countries
sub_count<- pkmng_countries %>% filter(NAME==c("France","Italy"))
sub_count_sf<- sf::st_as_sf(sub_count)
merge_count<- sf::st_combine(sub_count_sf)
plot(merge_count)
Geoprocessing: Dissolve
- Unifies boundaries based on common attribute values
diss_sub_count<- rgeos::gUnaryUnion(sub_count)
plot(diss_sub_count)
Zonal statistics
Allows you to calculate statistics from raster data for each feature a vector dataset
Example: Calculate mean temperature at pokemons were collected.
Zonal statistics: Step 1
Download and plot the mean annual temperature from the web using the getData function
climate <- raster::getData('worldclim', var='bio', res=10)
annual_temp<- climate$bio1
plot(annual_temp, col = viridis::inferno(n=10))
Zonal statistics: Step 2
Extract temperature at each point
poke_annual_temp<- raster::extract(annual_temp, pkmng_points, na.rm=T, df=T)
head(poke_annual_temp)
ID bio1
1 1 95
2 2 92
3 3 93
4 4 73
5 5 133
6 6 89
Zonal statistics: Step 3
Uh-oh, the data has no identification of which point it belongs! We need to merge it with the point identifiers.
poke_annual_temp_final<- cbind(pkmng_points@data$pokemonId,poke_annual_temp$bio1)
colnames(poke_annual_temp_final)<- c("pokemonId","Annual_Temp")
head(poke_annual_temp_final)
pokemonId Annual_Temp
[1,] 16 95
[2,] 60 92
[3,] 19 93
[4,] 16 73
[5,] 23 133
[6,] 120 89
Zonal statistics: Results
Ta-da!
boxplot(poke_annual_temp_final[,2], main = "Pokemon Collection Temperature")
Challenge 3: Zonal Statistics
Now you try it! What is the mean annual precipitation at which pokemons were collected?
Hint: Mean annual precipitation is bio12 in the climate dataset.
Solution 3: Zonal Statistics
annual_precip<- climate$bio12
poke_annual_precip<- raster::extract(annual_precip, pkmng_points, na.rm=T, df=T)
poke_annual_precip_final<- cbind(pkmng_points@data$pokemonId,poke_annual_precip$bio12)
colnames(poke_annual_precip_final)<- c("pokemonId","Annual_Precip")
paste0("The mean annual precipitation at pokemon collections: ",
round(mean(poke_annual_precip_final[,2], na.rm = T)), digits = 2)
[1] "The mean annual precipitation at pokemon collections: 9372"
TAKE HOME MESSAGES
- Be mindful of projections!
- Visualize spatial patterns!
- No one tool does everything!
