I am trying to divide a certain city into several blocks, each representing North, North-West, North-East, South...and so on. I just need the coordinates of the region boundaries (e.g.: North is between X and Y latitude and between Z and T longitude), so that I can check in my app whether a point belongs to a region or another. The regions should not depend on a certain zoom level's boundaries and they don't need to be the same size (maybe the North part of a city is a little bit larger then the South one).
Any idea how can I "draw" these region boundaries? Thank you!
For boundary data, you would have to do a search. Depends on the city and country. In the US, many municipalities provide this data directly through a city or country web site. Generally it will be in a GIS data format such as a shapefile. You have a number of different options for working programmatically with GIS data formats. I recommend using the GDAL libraries,
particularly ogr2ogr. Once you've got the boundary data, you can draw it on the map using polyline overlays or create a raster images of the data, say using gdal_rasterize. Or you can convert the data to KML using ogr2ogr, and upload it to Google Fusion Tables using Google Docs and overlay it using a FusionTablesLayer.
Related
In R, I am trying to create a choropleth map. I have built a database of businesses, some are part of chains (e.g. McDonalds) and others are independent. I want to calculate how many businesses are within 30km of each point on the map, but treat the different locations of chains as a single business.
For example, if you a point is:
5km from a McDonalds,
10km from Taco Bell
15km from Chick-Fil-A
20km from KFC
25km from McDonalds
35km from Five Guys
The colour will show that there are 4 fast food outlets within 30km.
I am happy to use any R package but I am mostly familiar with tmaps and ggplot2 maps.
At this stage the best approach I can think of is to create polygons for each chain and stack them as transparent layers of the same colour. I don't think this would be very efficient and wouldn't create a very nice looking choropleth.
The other answers I could find were either counting points (e.g https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r) or for GIS software.
EDIT:
I have managed to create a 30km radius from every location of every chain (using rgeos gIntersection). I now have a series of polygons.
To solve my question the additional thing I need to do is create polygons for where:
Only one polygon covers the area,
Two polygons covers the area,
etc.
To try to visual is this I used the answer from https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r
In the linked question they are trying to count how many polygons cover the numbered points (the image on the right). What I am trying to do is to create the image on the left, where there are polygons of no overlap (1), two overlapping polygons (2) and so on.
I think what you are trying to accomplish would be best approached using a raster approach rather than a chloropleth. To make a chorlopleth, you define a set of (generally irregular) polygons, summarize something within each polygon, then color the polygons based on the attributes. This would be a good approach if you wanted to say how many fast food resteraunts are within each state or county, or how many fast food joints per capita by state.
From your description, however, you are looking for how many fast food joints within a set radius for all points. This is more of a raster question, since you can represent your data on a regular grid.
The raster package is a good start for working with raster data and works well with the sf package.
You need to determine what density you need to accomplish your goal, then use this to determine the resolution of your raster. Once you've got that you can use raster::rasterize() to summarize your (I'm assuming) point data.
I'm assuming you have an object that has the locations of each restaurant, I'll call this object "points".
library(raster)
library(sf)
# create raster template with 30km resolution (I'm assuming your projection is in meters)
raster_template = raster((extent(points),
resolution = 30000,
crs = st_crs(points)
)
# rasterize your point data
r = rasterize(points, raster_template, fun = "count")
This should create a grid where each cell has the number of points within each 30km cell. You should then be able to plot the raster, but may want to either clip or mask it to just show parts that are within New Zealand
I am using the {UScensus2010} package in R and trying to get the area for each county. I found the areaPoly() in the package. Does anyone know the unit of the area? Is it square mile?
Thank you.
Assuming you are using US Census data, this is from the explanation of the dataset that UScensus2010 links to:
Land area measurement in square meters. The accuracy of the area
measurement is limited by the inaccuracy inherent in the mapping of
the various boundary features in the Census Bureau’s geographic
database. Land area includes areas classified as intermittent water,
swamps, and glaciers, which appear on census maps and in the Census
Bureau’s geographic database as hydrographic features. Square miles
can be derived by dividing square meters by 2,589,988. See Appendix A,
“Geographic Terms and Concepts,” for definition of this field.
http://www.census.gov/prod/cen2010/doc/sf1.pdf
If you are still unsure, pick your home county and check it against the area that wikipedia or the official county website claims.
I would like to add color to each house in the following google map example:
https://www.google.com/maps/#43.0748326,141.3479359,19z?hl=en
I found an example in leaflet that add colors to US states by giving the GeoJson data including the corners coordinates of each state. You can find the example here:
http://leafletjs.com/examples/choropleth.html
I would like to do it with Google Map. Two questions in front of me are:
1. How can I get the corner coordinates of houses?
2. As the number of houses which I want to add color is relatively large, how can I efficiently measure the corner coordinates of these houses?
How can I get the corner coordinates of houses?
You can't. The data in Google Maps API is not available to you, because of technical and legal restrictions.
You'll likely want to do this some other way: for instance, with OpenStreetMap data and one of the many services that visualizes it.
Here's my problem. I want to compare the area within multiple polygons in different parts of the world. I have the longitude and latitudes for each point of each polygon. My problem is that I don't know what projection to use to get x-y coordinates from the long-lat coordinates. I know OpenStreetMap has the projectMercator() function, but areas are known to inflate quite badly with latitude. (http://en.wikipedia.org/wiki/List_of_map_projections)
--> Do you guys know of an R function like projectMercator, that doesn't have such a distortion? I've been going over different types of projections in Wikipedia, but it's very unclear to me which is best for area comparisons, and then if those projections exist in R as functions (if they don't I'm fine hand coding them, though!)
Thanks!!!
Hillary
I have a large collection of pictures with GPS locations, encoded as lat/lon coordinates, mostly in Los Angeles. I would like to convert these to (1) zipcodes, and (2) neighborhood names. Are there any free web services or databases to do so?
The best I can come up with so far is scrape the neighborhood polygons from the LA times page and try to find out in which polygon every coordinate is. However this might be quite a lot of work, and not all of my coordinates are in LA. As for the zipcodes, this 2004 database is the best I can find, however zipcodes are encoded as a single coordinates instead of a polygon. So the best I can do is find the minimum distance from a given coordinate to the given zipcode-coordinates, which is not optimal.
I was under the impression that google-maps or open-street-maps should be able to do this (as they seem to 'know' exactly where every neighboorhood and zipcode is), however I cannot find any API's to do the lookups / queries.
You can now do this directly within R itself thanks to the rather awesome ggmap package.
Like others mention, you'll be reverse geocoding using the google maps API (and therefore limited to 2,500 queries daily), but it's as simple as:
library("ggmap")
# generate a single example address
lonlat_sample <- as.numeric(geocode("the hollyood bowl"))
lonlat_sample # note the order is longitude, latitiude
res <- revgeocode(lonlat_sample, output="more")
# can then access zip and neighborhood where populated
res$postal_code
res$neighborhood
Use Reverse Geocoding to convert your lat/lon to addresses. It has some limit on the number of queries per day though.
Here is a nice blog post with examples how to geocode and reverse geocode using google-maps.
Try this one:
http://www.usnaviguide.com/zip.htm
There is some limit as to how many queries per day you can do on the site, but they also sell the complete database, which changes every few months.
Sorry that I don't know of any free resources.
As others suggested, geocode them into street address should work fine for zip code. i am not too sure about neighborhood, because you may have to look if street number is odd/even to see if it is located which side of a road that determines neighborhood.
An alternative way is to prepare GIS polygon feature (ESRI shape file for example), test each point against this set of polygons see which one it intersects.
zip code is very straighforward, you can download shape file from the census.
http://www.census.gov/cgi-bin/geo/shapefiles2010/main
neighborhood is harder, i'd guess. In another part of US i had to create my shape file on my own by combining definitions from municipal government, real-estate website, newspaper etc so that it looks like what people thinks neighborhood in the city are without having any overlap or gap. It can take some time to compose such set of polygons. you may crab census "block group", or even census "block" from the above page and merge them
Once you prepared polygon features, there are couple of GIS tools on different environment (stand-alone executable, GUI program, c/python/sql etc API, probably R as well, to do intersection of polygons and points.