mobile geo social

GeoMeme is a web app (and also a mobile web app for iPhone and Android) that I recently developed as a pet project. It measures real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that beats in San Francisco:

A large amount of geo-data is generated by GeoMeme, and so arises a need shared by many geo apps: scalable, fast, and accurate spatial queries, used to select a subset of geo-data for display as markers on a map, or on Google Earth.

Google App Engine

Google App Engine is an obvious choice for hosting your geo app. The App Engine datastore is built on top of Google’s BigTable technology which scales very well, and is optimized for fast data retrieval. And it doesn’t cost the earth like some traditional GIS database solutions.

Inequality constraint

If you are coming from a background of relational databases, you might think the solution here would be to store the latitude and longitude of all your markers in a database table, and do a simple query to retrieve only those contained within the bounds of the map.

However, the flipside of being optimized for fast data retrieval is that BigTable only allows inequality filters on a single dimension, to avoid the burden of full table scans. For example, the following form of spatial query is not supported because it specifies inequality filters on both latitude and longitude dimensions:

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