Geovisualisation
Using the Google Places API with a Database and Visualizing Data on Google Map
In this project, we are using the Google geocoding API to clean up some user-entered geographic locations of university names and then placing the data on a Google Map.
Note1: Windows has difficulty in displaying UTF-8 characters in the console so for each command window you open, you may need to type the following command before running this code:
chcp 65001
http://stackoverflow.com/questions/388490/unicode-characters-in-windows-command-line-how
You should install the SQLite browser to view and modify the databases from:
Note2: Alter the where.data file and put the deisred location to be visualized.
The first problem to solve is that the Google geocoding API is rate limited to a fixed number of requests per day. So if you have a lot of data you might need to stop and restart the lookup process several times. So we break the problem into two phases.
In the first phase we take our input data in the file (where.data) and read it one line at a time, and retrieve the geocoded response and store it in a database (geodata.sqlite). Before we use the geocoding API, we simply check to see if we already have the data for that particular line of input.
You can re-start the process at any time by removing the file geodata.sqlite
Run the geoload.py program. This program will read the input lines in where.data and for each line check to see if it is already in the database and if we don't have the data for the location, call the geocoding API to retrieve the data and store it in the database.
As of December 2016, the Google Geocoding APIs changed dramatically. They moved some functionality that we use from the Geocoding API into the Places API. Also all the Google Geo-related APIs require an API key. To complete this assignment without a Google account, without an API key, or from a country that blocks access to Google, you can use a subset of that data which is available at:
http://py4e-data.dr-chuck.net/json
To use this, simply leave the api_key set to False in geoload.py.
This URL only has a subset of the data but it has no rate limit so it is good for testing.
If you want to try this with the API key, follow the instructions at:
https://developers.google.com/maps/documentation/geocoding/intro
and put the API key in the code.
Here is a sample run after there is already some data in the database:
Mac: python3 geoload.py Win: geoload.py
Found in database Northeastern University
Found in database University of Hong Kong, Illinois Institute of Technology, Bradley University
Found in database Technion
Found in database Viswakarma Institute, Pune, India
Found in database UMD
Found in database Tufts University
Resolving Monash University Retrieving http://py4e-data.dr-chuck.net/json?key=42&address=Monash+University Retrieved 2063 characters { "results" : [ {u'status': u'OK', u'results': ... }
Resolving Kokshetau Institute of Economics and Management Retrieving http://py4e-data.dr-chuck.net/json?key=42&address=Kokshetau+Institute+of+Economics+and+Management Retrieved 1749 characters { "results" : [ {u'status': u'OK', u'results': ... }
The first five locations are already in the database and so they are skipped. The program scans to the point where it finds un-retrieved locations and starts retrieving them.
The geoload.py can be stopped at any time, and there is a counter that you can use to limit the number of calls to the geocoding API for each run.
Once you have some data loaded into geodata.sqlite, you can visualize the data using the (geodump.py) program. This program reads the database and writes tile file (where.js) with the location, latitude, and longitude in the form of executable JavaScript code.
A run of the geodump.py program is as follows:
Mac: python3 geodump.py Win: geodump.py
Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA 42.3396998 -71.08975 Bradley University, 1501 West Bradley Avenue, Peoria, IL 61625, USA 40.6963857 -89.6160811 ... Technion, Viazman 87, Kesalsaba, 32000, Israel 32.7775 35.0216667 Monash University Clayton Campus, Wellington Road, Clayton VIC 3800, Australia -37.9152113 145.134682 Kokshetau, Kazakhstan 53.2833333 69.3833333 ... 12 records written to where.js Open where.html to view the data in a browser
The file (where.html) consists of HTML and JavaScript to visualize a Google Map. It reads the most recent data in where.js to get the data to be visualized. Here is the format of the where.js file:
myData = [ [42.3396998,-71.08975, 'Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA'], [40.6963857,-89.6160811, 'Bradley University, 1501 West Bradley Avenue, Peoria, IL 61625, USA'], [32.7775,35.0216667, 'Technion, Viazman 87, Kesalsaba, 32000, Israel'], ... ];
This is a JavaScript list of lists. The syntax for JavaScript list constants is very similar to Python so the syntax should be familiar to you.
Simply open where.html in a browser to see the locations. You can hover over each map pin to find the location that the gecoding API returned for the user-entered input. If you cannot see any data when you open the where.html file, you might want to check the JavaScript or developer console for your browser.