This document discusses building location-aware applications using MongoDB and geospatial querying. It proposes a mobile app called Pulse that helps people in emergency situations by finding the closest hospital and notifying emergency contacts. It then demonstrates how to store location data in MongoDB, create a 2D index on the location field, and perform geospatial queries to find hospitals near a given location. The document notes some current limitations of geospatial queries in MongoDB and provides examples of different geospatial operators.

1. Location Based Services Lalit Kapoor - @idefine Washington, DC Monday, June 27, 2011

2. MongoDB Is Easy! Document Object Store JSON (BSON) Schema-less Multiple language support (c++ to python to nodejs) Easy-to-Scale (http://www.mongodb.org/display/DOCS/Production+Deployments) Support for geospatial indexing and queries

3. Building Your First Location Aware App Pulse Mobile

4. Pulse Help people in emergency situations Find the closest hospital Notify important contacts (immediate family, friends, primary care physician, etc) Extra features that would make this app rock! Provide real-time updates of your location to hospital and contacts Send pictures to hospital Get ER wait times (this data is available in some states) Notify hospital of important medical information: blood-type, allergies, current medications, EMR, etc (integrate with google health/careverge) Integrate with 3rd party devices (watches, wristbands, etc) that know your vitals such as: blood pressure and heart rate. Transmit to your smart phone and then to hospital and contacts

5. LBS w/MongoDB Get or collect some location data (addresses are fine) Store it in mongoDB if data is in tsv/csv/json use mongoimport find lat/lng if you don’t already have it use Google’s MAP API to find lat/long if you have an address build a 2d index (lat/lng) db.collection.ensureIndex({field:”2d”}); let the geospatial queries begin!

6. Get Some Data Location of Hospitals In The United States Medicare.gov has information about the quality of care at each hospital - this data also happens to include the address for over 4000 hospitals in the US

7. Import into MongoDB mongoimport --headerline --type csv --drop --db mongodc --collection hospitals --file OutcomeofCareMeasures.csv

8. Geospatial Indexing db.places.ensureIndex({loc:”2d”}); / (compounded indexes): db.places.ensureIndex( { loc : "2d" , category : 1 } ); Geohash is calculated and stored in a B-Tree Sharding Capable (as of 1.7.2 - Thanks Mathias - from 10gen) Current Limitations “Doesn’t currently handle wrapping at the poles or at the transition from -180° to +180° longitude, however we detect when a search would wrap and raise an error” (Resolved in 1.9+) Need to use geoNear command instead of $near for now when sharding (see JIRA SERVER-1981) - Proposed for resolution in 2011 geoNear not in the pymongoDrivers! Eval Hack - http://permalink.gmane.org/gmane.comp.db.mongodb.user/67 (Thanks smsfail on #mongodb) eval_result = db.eval('db.runCommand( { geoNear : "location" , near :[%s,%s], num : %s } );' % (lat, lon, num)) results = eval_result.get('results', []) Do not shard on a geo key - works, but inefficient. See JIRA SERVER-1982.

9. Geospatial Querying geoNeargives you back the distance  (in radians if sphere=true) Results are sorted by distance away from points $near vs. $nearSphere (1.7.0) $within [box, center, polygon] instead of $near Not sorted by distance More examples and details on the wiki

10. Questions? Lalit Kapoor - @idefine