| State | Under 5 Years | 5 to 13 Years | 14 to 17 Years | 18 to 24 Years | 25 to 44 Years | 45 to 64 Years | 65 Years and Over | |
|---|---|---|---|---|---|---|---|---|
| AL | 310504 | 552339 | 259034 | 450818 | 1231572 | 1215966 | 641667 | |
| AK | 52083 | 85640 | 42153 | 74257 | 198724 | 183159 | 50277 | |
| AZ | 515910 | 828669 | 362642 | 601943 | 1804762 | 1523681 | 862573 | |
| AR | 202070 | 343207 | 157204 | 264160 | 754420 | 727124 | 407205 | |
| CA | 2704659 | 4499890 | 2159981 | 3853788 | 10604510 | 8819342 | 4114496 | |
| CO | 358280 | 587154 | 261701 | 466194 | 1464939 | 1290094 | 511094 | |
| CT | 211637 | 403658 | 196918 | 325110 | 916955 | 968967 | 478007 | |
| DE | 59319 | 99496 | 47414 | 84464 | 230183 | 230528 | 121688 | |
| DC | 36352 | 50439 | 25225 | 75569 | 193557 | 140043 | 70648 |
| AL | 310504 | 552339 | 259034 | 450818 | 1231572 | 1215966 | 641667 | |
|---|---|---|---|---|---|---|---|---|
| AK | 52083 | 85640 | 42153 | 74257 | 198724 | 183159 | 50277 | |
| AZ | 515910 | 828669 | 362642 | 601943 | 1804762 | 1523681 | 862573 | |
| AR | 202070 | 343207 | 157204 | 264160 | 754420 | 727124 | 407205 | |
| CA | 2704659 | 4499890 | 2159981 | 3853788 | 10604510 | 8819342 | 4114496 | |
| CO | 358280 | 587154 | 261701 | 466194 | 1464939 | 1290094 | 511094 | |
| CT | 211637 | 403658 | 196918 | 325110 | 916955 | 968967 | 478007 | |
| DE | 59319 | 99496 | 47414 | 84464 | 230183 | 230528 | 121688 | |
| DC | 36352 | 50439 | 25225 | 75569 | 193557 | 140043 | 70648 |
This Python script utilizes the GeoPy geocoding library to batch geocode a number of addresses, using various services until a pair of latitude/longitude values are returned. Python 3 port and refactor of a script by @rgdonohue.
https://gist.github.com/ericmhuntley/0c293113aa75a254237c143e0cf962fa
Built to anticipate an input csv should that includes columns named street, city, state, country.
| require('tigris') | |
| require('tidyr') | |
| require('dplyr') | |
| require('sf') | |
| options(tigris_use_cache=FALSE) | |
| tracts <- tracts("MA", year = 2013) %>% | |
| st_as_sf() %>% | |
| mutate(cens_land = as.numeric(ALAND) * 10.76391) %>% |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>Linking Leaflet Layers by ID</title> | |
| <style> | |
| #map {height: 850px;} | |
| </style> | |
| <link rel="stylesheet" href="https://unpkg.com/leaflet@1.4.0/dist/leaflet.css" /> | |
| <!-- Must come after leaflet.css --> |
| var nwCorner = L.latLng(42.341055,-71.07289), | |
| seCorner = L.latLng(42.398887,-70.948951), | |
| bounds = L.latLngBounds(nwCorner, seCorner) | |
| // Create Leaflet map object | |
| var map = L.map('parcelMap',{ | |
| center: [42.371353, -71.034150], | |
| zoom: 15, | |
| minZoom: 14, |
| # | |
| # In this global.R field, we create all objects and run all code | |
| # that we only want to run once when we start our app and that we | |
| # want accessible to all parts of our app. This includes database | |
| # queries (in this case at least), API keys, etc. | |
| # | |
| # First, we load and attach packages. A package must be installed before | |
| # loading; for example, to install RPostgreSQL, we simply type | |
| # install.packages("RPostgreSQL"). | |
| # |
| import os | |
| import glob | |
| import geojson | |
| json_dir_name = "./" | |
| json_pattern = os.path.join(json_dir_name,'*.geojson') | |
| file_list = glob.glob(json_pattern) | |
| collection = [] |
| from qgis import * | |
| import processing | |
| proj_path = QgsProject.instance().readPath("./") | |
| layers = iface.mapCanvas().layers() | |
| for n, i in enumerate(layers): | |
| ext = i.extent() | |
| size = 5280 |
| require('tidyverse') | |
| require('tidycensus') | |
| library('jsonlite') | |
| require('sf') | |
| census_api_key("<yourapikey>") | |
| stops <- fromJSON("https://api-v3.mbta.com/stops/?filter[route]=Red", simplifyVector = TRUE)[[1]]$attributes %>% | |
| st_as_sf(coords = c('longitude', 'latitude')) %>% | |
| st_set_crs(4326) %>% |
