Count coords dots in polygon - json

I would like to count the appearance of several dots in a prespecified polygons. I am loading the EU NUTS Region by
nuts = 'https://raw.githubusercontent.com/eurostat/Nuts2json/master/2016/4258/60M/nutsrg_2.json'
geo_json_nuts = json.loads(requests.get(nuts).text)
and I have a list of tuples or a DataFrame, which contains data as follows:
Index lon lat
0 -178.1328187 -14.3087256
1 -176.2036596 -13.3469813
2 -176.1720255 -13.2789922
3 -151.3381037 -22.4532474
4 -151.0331577 -16.7159449
... ... ...
Now I would like to match the lon/lat in the DataFrame to the Feature properties id contained in geo_json_nuts. Meaning if lon/lat is contained in one of the polygons in geo_json_nuts it should get the properties id, e.g. BE31 or AT32, etc.
Does anyone know how to handle this?
Thank you in advance!
Best regards
Alex


you can try to normalize your json file.
nuts = 'https://raw.githubusercontent.com/eurostat/Nuts2json/master/2016/4258/60M/nutsrg_2.json'
geo_json_nuts = json.loads(requests.get(nuts).text)
df = json_normalize(geo_json_nuts, 'features', ['coordinates'],
errors='ignore',
record_prefix='features_')
After this process, you will have a dataframe contains 'features_properties.id' for the id and the features_geometry.coordinates for the points.

Related

Gets all polygons from a json file

I have a json file, with the coordinates of several polygons. What I want to do is automate the getting the coordinates but saving them separately by polygons. In the file that I currently use, I have 2 polygons:
{"type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[
[[[0.03990083369766674,41.65117297365802],[0.03986585098450487,41.6511738730363],[0.0398193071116094,41.65118861345209],[0.03978640897789055,41.651211227828185],[0.039785212880236676,41.65121205009294],[0.039761889395081365,41.65124414021116],[0.03975127958141444,41.65127097990489],[0.03973309671778063,41.65131697476312],[0.039706482497244025,41.651425988964064],[0.03969658765996025,41.65146652049745],[0.039683272167788275,41.65150292059053],[0.03968380609505212,41.65151410540278],[0.03968455879000179,41.6515298809837],[0.03969278143750318,41.65153853529924],[0.03969588944738445,41.651541805918015],[0.03971376972429572,41.65156062329174],[0.039752407785648564,41.65158215221135],[0.03979465677110295,41.65160350427274],[0.03988602119749911,41.651632606242316],[0.03992955596697664,41.65164948823451],[0.03993958742929026,41.65165619711021],[0.039968351608118506,41.651675434417314],[0.03999355934521909,41.651701840766634],[0.04000585559790465,41.65173745966629],[0.04000155651951174,41.65177364266802],[0.03998752269819514,41.65181002432095],[0.03993384247251406,41.65191499844431],[0.0399064931590255,41.65198778102845],[0.03987869457550017,41.65209657478536],[0.03987154648804941,41.65213727647139],[0.03986267424301351,41.65216458136081],[0.039847923768933036,41.65220997439829],[0.03980755819723356,41.65227854926681],[0.03978735697119576,41.65231062932676],[0.0397695957173166,41.65234709479867],[0.039757844288369676,41.65238344543852],[0.03975297859328857,41.65242411611149],[0.03975461138812371,41.65245838049552],[0.039754692692589313,41.652460093756645],[0.03976241996958375,41.65249586520693],[0.039775312175620324,41.65253159055675],[0.039792208417040606,41.65257165102716],[0.0398126778639959,41.65260712072893],[0.03984532034965278,41.652678297340344],[0.039888420936520154,41.652749203216246],[0.039899750755712435,41.652784978019326],[0.03990246230155005,41.65282539304425],[0.03990100971764384,41.65284035977143],[0.03989973315371473,41.652853510978275],[0.03989900644266644,41.65286150731441],[0.03988340855812481,41.65289793925871],[0.03982299431103434,41.65300310029855],[0.03980279308499652,41.653035180358444],[0.039796679324458795,41.65304764676378],[0.03978491196989489,41.653071643315826],[0.0397440929372068,41.65317631904341],[0.0397286140756973,41.65321275350229],[0.03970857042944851,41.65324925082547],[0.03966616386420511,41.6533061086308],[0.03966059408921564,41.6533135760688],[0.03962721818698753,41.653337031149846],[0.039608159414420085,41.65335700606448],[0.03959644067489746,41.653369288906106],[0.039574675385634625,41.65340141925745],[0.03955691413175548,41.653437884729385],[0.03954504200339567,41.653474232016464],[0.03952742659464041,41.65350945529025],[0.03952465972823856,41.65351498818487],[0.03951080444147017,41.653542693729264],[0.03949758031204822,41.6535615806728],[0.03948831998487233,41.6535748056404],[0.03938851917243118,41.65370353659338],[0.039367413538987255,41.65375064205381],[0.039358610025561444,41.653770290912405],[0.039341569615398024,41.653808324638504],[0.03932925156976292,41.65384916294957],[0.039294849722525456,41.653976004621455],[0.03927882436089661,41.65403509285139],[0.03927713624549725,41.654056445750996],[0.03927596780812548,41.65407122304717],[0.039281407663608055,41.65410711603502],[0.039310323554892744,41.654178376465495],[0.03936399288409906,41.65425614795873],[0.039382605739391195,41.65428311924833],[0.03939218541709694,41.654297000518994],[0.0394054858218423,41.65431627386832],[0.03942630395599031,41.65434644117781],[0.039450928312595376,41.6544222747749],[0.03945708817360331,41.65445818620299],[0.039456164487818746,41.654516564485675],[0.03945594907289441,41.654530202681144],[0.039453803305554584,41.65455188918051],[0.03945192324456115,41.65457089514709],[0.03943704788011598,41.65460725500702],[0.03941627919919963,41.65464382357635],[0.03934394253232732,41.654732627331185],[0.0393319027658941,41.654747407980096],[0.039311307590383746,41.65477269199252],[0.03926260956824479,41.6548369987957],[0.03925610688737672,41.65484747114613],[0.03921995489865273,41.65490569352545],[0.03921109941742416,41.65492864736862],[0.0392059194009554,41.6549420751783],[0.03919861708647711,41.65497826991475],[0.03919375139139601,41.65501894058772],[0.039193012107492216,41.655039557556165],[0.03919245806365955,41.655055020492085],[0.03919789791914211,41.655090913479995],[0.03921464077172523,41.65512646783898],[0.03923784355746767,41.65515732497917],[0.03926289874392143,41.65517922521707],[0.03928896814071942,41.655192054558825],[0.03933606186649572,41.65518633390957],[0.0394175440284636,41.655148178645845],[0.039442782778607764,41.65513278192702],[0.03945621058828894,41.65512459029256],[0.039527098862206865,41.65506869053877],[0.039586379037730636,41.65501163156773],[0.039623324792732284,41.654976069665025],[0.039725422246779336,41.654896838882365],[0.03976381052740279,41.654868829913084],[0.03980034640731491,41.65485428311925],[0.039848573366467556,41.654835081854316],[0.039941219385935034,41.6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Here starts second polygon
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]}}
saved as poly1.json
Is there any way to save them separately, I would like the final result to be an like this:
array = [coordinates of polygon 1], [coordinates of polygon 2] where in the case of using more polygons than in the example, they will be saved automatically without having to change the code, array = [coordinates of polygon 1], [coordinates of polygon 2],[coordinates of polygon 3] and so on.
So far i have this :
require 'rubygems'
require 'json'
file = File.read('poly1.json')
coordinates = JSON.parse(file)
string=coordinates.to_s[0...-4]
path = string.split"["
path.shift()
path.shift()
path.shift()
This just gets all coordinates from both polygons in path. But what i want is to get polygon 1 coordinates separated from polygon 2 coordinates and in a case where i use a 3 polygon file, to get it separated whit out doing manualy.
Sorry for my bad english, and if you need something else ask in comments.

Your example is quite big and therefore not easy to understand. But from key structure at the beginning of the file
{"type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[ [[[0.03990083369766674,41.65117297365802], ...
it looks like the coordinates are a simple hash nested below the geometry key. Therefore I would try the following:
require 'rubygems'
require 'json'
file = File.read('poly1.json')
data = JSON.parse(file)
coordinates = data["geometry"]["coordinates"]
coordinates[0] #=> should now return the first coordonate from the array

R: Web scraping JSON, extracting information from nest

I am trying to use tidyJSON to extract information from JSON, but I am open to any R package that can achieve my ends. I took a look at the documentation and vignittes and found the complex example was helpful. However, the information I want is nested inside of a non-key-value pair and I am not sure how to access it. I am interested in getting appid, name, developer, etc., but this information is within 570 and 730:
{"570":{"appid":570,"name":"Dota 2","developer":"Valve","publisher":"Valve","score_rank":71,"owners":102151578,"owners_variance":259003,"players_forever":102151578,"players_forever_variance":259003,"players_2weeks":9436299,"players_2weeks_variance":89979,"average_forever":11727,"average_2weeks":1229,"median_forever":277,"median_2weeks":662,"ccu":811259,"price":"0","tags":{"Free to Play":22678,"MOBA":7808,"Strategy":7415,"Multiplayer":6757,"Team-Based":4848,"Action":4602,"e-sports":4089,"Online Co-Op":3669,"Competitive":3553,"PvP":2655,"RTS":2267,"Difficult":2129,"RPG":2114,"Fantasy":2044,"Tower Defense":2024,"Co-op":1898,"Character Customization":1514,"Replay Value":1487,"Action RPG":1397,"Simulation":1024}},
"730":{"appid":730,"name":"Counter-Strike: Global Offensive","developer":"Valve","publisher":"Valve","score_rank":78,"owners":29225079,"owners_variance":154335,"players_forever":28552354,"players_forever_variance":152685,"players_2weeks":9102348,"players_2weeks_variance":88410,"average_forever":17648,"average_2weeks":791,"median_forever":5030,"median_2weeks":358,"ccu":543626,"price":"1499","tags":{"FPS":17082,"Multiplayer":13744,"Shooter":12833,"Action":10881,"Team-Based":10369,"Competitive":9664,"Tactical":8529,"First-Person":7329,"e-sports":6716,"PvP":6383,"Online Co-Op":5714,"Military":4621,"Co-op":4435,"Strategy":4424,"War":4361,"Realistic":3196,"Trading":3191,"Difficult":3158,"Fast-Paced":3100,"Moddable":2496}}
There are many thousands of such entries. Is there a way to skip the "top-level" and look within the nest?
The JSON information is from http://steamspy.com/api.php?request=top100in2weeks

This might be what you need:
library(jsonlite)
data = fromJSON("http://steamspy.com/api.php?request=top100in2weeks")
appid = lapply(data, function(x){x$appid})
name = lapply(data, function(x){x$name})
df = data.frame(appid = unlist(appid),
name = unlist(name),
stringsAsFactors = F)
Result:
> head(df)
appid name
570 570 Dota 2
730 730 Counter-Strike: Global Offensive
578080 578080 PLAYERUNKNOWN'S BATTLEGROUNDS
440 440 Team Fortress 2
271590 271590 Grand Theft Auto V
433850 433850 H1Z1: King of the Kill
I'll let you add the rest of the information
Edit: Adding arrays to a dataframe
Adding the tags information for each game in the data frame is possible. And the times tagged as well. For each game you must store an array of tag names in a column and the tag quantities in another.
After the definition of df add the following lines:
for(k in 1:nrow(d)){
d$tags[k] = list(names(data[[k]]$tags))
d$tagsQ[k] = list(unlist(data[[k]]$tags))
}
This will give you:
> d["570",]
appid name
570 570 Dota 2
tags
570 Free to Play, MOBA, Strategy, Multiplayer, Team-Based, Action, e-sports, Online Co-Op, Competitive, PvP, RTS, Difficult, RPG, Fantasy, Tower Defense, Co-op, Character Customization, Replay Value, Action RPG, Simulation
tagsQ
570 22686, 7810, 7420, 6759, 4850, 4603, 4092, 3672, 3555, 2657, 2267, 2130, 2116, 2045, 2024, 1898, 1514, 1487, 1397, 1023
In this situation, columns tags and tagsQ contain lists. To obtain the second tag and quantity for appid 570 do:
> df["570","tags"][[1]][2]
[1] "MOBA"
> d["570","tagsQ"][[1]][2]
MOBA
7810

How different colors to different sections of a route on leaflet map? [R Studio]

I have a JSON file of a long route. The file contains the lat and long of of this route.
I'm trying to mark different sections of this route based on a set of criteria (which I've compiled in a dataframe). However, I'm facing to problems:
1) How do I break up this long set of lat and longs into segments? (can't do this manually because I have many route variations)
2) How do I assign a variable color to each segment?
I intend to use leaflet map (for its interactivity), but I'm open to better suggestions.

When working with spatial data, it helps to know spatial classes! I am assuming you know hoe to read your JSON file as a data frame into R.
Here's a reproducible example:
library(mapview)
library(sp)
### create some random data with coordinates from (data("breweries91", package = "mapview"))
set.seed(123)
dat <- data.frame(val = as.integer(rnorm(32, 10, 2)),
lon = coordinates(breweries91)[, 1],
lat = coordinates(breweries91)[, 2])
### state condition for creation of lines
cond <- c(8, 9, 10)
### loop through conditions and create a SpatialLines object for each condition
lns <- lapply(seq(cond), function(i) {
ind <- dat$val == cond[i]
sub_dat <- dat[ind, ]
coords <- cbind(sub_dat$lon, sub_dat$lat)
ln <- coords2Lines(coords, ID = as.character(cond[i]))
proj4string(ln) <- "+init=epsg:4326"
return(ln)
})
### view lines with mapview
mapview(lns[[1]], col = "darkred") +
mapview(lns[[2]], col = "forestgreen") +
mapview(lns[[3]], col = "cornflowerblue")
Essentially, what we are doing here is create a valid sp::SpatialLines object for each condition we specify. The we plot those using mapview given you mentioned interactivity. Plotting of spatial objects can be achieved in many ways (base, lattice, ggplot2, leaflet, ...) so there's many options to choose. Have a look at sp Gallery for a nice tutorial.
Note: This answer is only valid for non-projected geographic coordinates (i.e. latitude/longitude)!

Check if multipolygon contains point in GeoDjango

I have a MultiPolygon field, preferences.locations, and a Point field, rental.location. When querying to check whether a rental's location is contained by preferences.locations, the query is only successful if the rental.location is contained by the first polygon in the preferences.locations MultiPolygon.
For example, with these geometries:
point1 = (81.20141954209073, -129.891357421875)
point2 = (40.70875101828792, -73.93179774284363)
preferences.locations = MultiPolygon(
Polygon(((81.14748070499664, -163.289794921875),
point1, # contains the first point
(81.14748070499664, -163.289794921875),
(81.14748070499664, -163.289794921875),)),
Polygon(((40.70718949655447, -73.98123621940613),
point2, # contains the second point
(40.683762276904055, -73.99702906608582),
(40.70718949655447, -73.98123621940613),)),
)
rental1.location = Point(*point1)
rental2.location = Point(*point2)
When querying to check which rentals' locations are contained by preferences.locations, while both rentals should be returned, only the first rental is returned.
>>> Rental.objects.filter(location__contained=preferences.locations)
[<Rental: Rental object>] # rental1
How can I successfully check which rentals' locations are contained by preferences.locations (no matter which Polygon they're contained by).

The proper way to check whether a Point is contained by a MultiPolygon is to use point.intersects(multipolygon).
>>> Rental.objects.filter(location__intersects=preferences.locations)
[<Rental: Rental object>, <Rental: Rental object>]

MeteorJS and UnderscoreJS: grouping results to plot points on a Google Map

I am working on a small MeteorJS app that plots points on a map based on popular areas for work.
I have this:
Template.list.jobs = function() {
if(Session.get('currentIndustryOnet')) {
jobs = Jobs.find({onet: Session.get('currentIndustryOnet')}).fetch();
// Session.set('jobCount', jobs.count());
var cnt = _.groupBy(jobs, 'address');
console.log(cnt);
return Pagination.collection(jobs);
} else {
jobs = Jobs.find()
Session.set('jobCount', jobs.count());
return Pagination.collection(jobs.fetch());
}
}
The cnt variable returns a properly grouped array (the key of the array is an address like Allentown, PA). I have a collection of Cities which have ever city in the USA along with their LAT/LONGs to plot on a Google Map. So I will take the top 100 from the grouped array, find the lat/long in the Cities collection and plot those points on a map.
I am not familiar with working with a groupedBy method to sort the list based on the length and then pull out the key to use as my search.

I'm not 100% certain about the data structure... but assuming jobs have an address field, and you want them sorted by frequency of occurrence and capped, you could do something like this:
var addresses = _.chain(jobs)
.countBy('address')
.pairs()
.sortBy(function(j) {return -j[1];})
.map(function(j) {return j[0];})
.first(100)
.value();
Note there may be a more clever way to use underscore to arrive at this result. Once you have the capped, sorted list of addresses, you can probably get the lat/long values via a find like:
Cities.find({address: {$in: addresses}}).fetch();