| --- | |
| title: Spatial Queries on a Feature Layer | |
| description: Create detailed spatial queries like point within polygon or line intersects polygons with Feature Layer queries. Some combinations will not result in any features being highlighted. | |
| layout: example.hbs | |
| --- | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <!-- Load Leaflet from CDN--> | |
| <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.css" /> |
You're probably better off just using this: the python library used there seems to handle errors better https://gist.github.com/buckleytom/0ecfbbb546913c7d9814
However, if you really want to:
This script uses RCurl and RJSONIO to download data from Google's API to get the travel time and distance between latitudes and longitudes by mode, based on some data in a csv
| import googlemaps | |
| import os | |
| import csv | |
| import pandas | |
| from array import array | |
| import numpy as np | |
| gmaps = googlemaps.Client(key=os.environ['GMAPSKEY']) | |
| df_s = pandas.io.parsers.read_csv('tripdata.csv',sep=',') |
| Cython==0.21 | |
| DataShape==0.3.0 | |
| Flask==0.10.1 | |
| GeoAlchemy2==0.2.4 | |
| Jinja2==2.7.3 | |
| MarkupSafe==0.23 | |
| PIL==1.1.7 | |
| PyYAML==3.11 | |
| Pygments==1.6 | |
| SQLAlchemy==0.9.7 |
The reader is advised to check out the official docs first, which are more comprehensive and accurate https://udst.github.io/urbansim/examples.html
One of the features of UrbanSim is regression.
Here we provide an example of how to write the configuration that allows you to tinker with the regression for residential property values in UrbanSim for the SF Bay Area.
At the end you should be able to change the specification for regression for residential housing (hedonic regression). While its not required for you to have used R before, we occasionally refer to R's lm function , because its a standard that many people are familiar with.
| import csv | |
| file1 = "fromAbag.csv" | |
| file2 = "tazData2010.csv" | |
| def sort_lowercase_headers(csvfile): | |
| csvreader = csv.reader(csvfile, delimiter=',',quotechar='|') | |
| header=csvreader.next() | |
| header.sort() | |
| header = [x.lower() for x in header] | |
| return header |
| [ | |
| { | |
| "url": "https://api.github.com/repos/MetropolitanTransportationCommission/bayarea_urbansim/pulls/124", | |
| "id": 63919159, | |
| "html_url": "https://github.com/MetropolitanTransportationCommission/bayarea_urbansim/pull/124", | |
| "diff_url": "https://github.com/MetropolitanTransportationCommission/bayarea_urbansim/pull/124.diff", | |
| "patch_url": "https://github.com/MetropolitanTransportationCommission/bayarea_urbansim/pull/124.patch", | |
| "issue_url": "https://api.github.com/repos/MetropolitanTransportationCommission/bayarea_urbansim/issues/124", | |
| "number": 124, | |
| "state": "closed", |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Ocean</title> | |
| <!-- Load Leaflet from CDN--> | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/leaflet/1.0.0-rc.3/leaflet.css" /> | |
| <script src="https://cdn.jsdelivr.net/leaflet/1.0.0-rc.3/leaflet-src.js"></script> | |
| <!-- Load Esri Leaflet from CDN --> | |
| <script src="https://cdn.jsdelivr.net/leaflet.esri/2.0.2/esri-leaflet.js"></script> |
| from smartsheet import * | |
| import pandas as pd | |
| from credentials import smartsheet_token | |
| smartsheet = smartsheet.Smartsheet(smartsheet_token) | |
| def get_sheet_as_df(sheet_id): | |
| ss1 = smartsheet.Sheets.get_sheet(sheet_id, page_size=0) | |
| row_count = ss1.total_row_count | |
| ss1 = smartsheet.Sheets.get_sheet(sheet_id, page_size=row_count) |
| <html> | |
| <head> | |
| <title>Source Zoning Data</title> | |
| <body>this is an empty page that will be used to redirect to a map in the future</body> |
