Dracones: Web-Based Mapping and Spatial Analysis for Public Health

Surveillance

Christian Jauvin

David Buckeridge

McGill University

Summary

Dracones:Built with MapServer/PostGIS

We'll be covering:Public Health context Software architectureSome specific problems

Public Health - Two Perspectives

Case management Individual cases of notifiable diseasesRelationship networks

Population surveillanceLarger risk patterns

Case Management

Questions/problems: Is a case due to recent transmission? If so, does the case share any feature with

other, recent cases?

Ways it's being done: Investigations/interviewsMeeting with other investigators

Population Surveillance

Questions/problems:Are more cases happening than expected?Does an excess suggest ongoing

transmission in a specific region?

Way it's being done:Semi-automated routine temporal and space-

time statistical analysis (SaTScan)

Montreal DSP

Département de santé publique de Montréal (Public Health Agency)

Need: incorporate spatial data + analysis capabilities within workflow

One reason: research shows that spatial information helps

Answer: Dracones project Funded in part by GeoConnections Led by David Buckeridge, MD, PhD 15 month contract

Case Management at the DSP

Current Situation Information on paper

entered into system (Oracle DB + Forms)

System contains sensitive data (names, addresses)

Limited tools for analyzing case data

Project Goal Capture spatial data Visualize and analyze

spatial distribution of cases

Population Surveillance at the DSP

Current Situation Routine temporal and

space-time statistical analysis

Capacity to visualize time-series but not maps

Project Goal Add mapping capacity Extend range of

analytic methods

Why Location Matters - Case Management

If you are studying a case of a certain disease that was just declared

It is harder to picture the situation by looking at something as this..

Why Location Matters - Case Management

Why Location Matters - Case Management

Than by looking at this..

Why Location Matters - Case Management

Why Location Matters - Population Surveillance

If you are studying the spatial distribution of a set of disease clusters

This would seem more difficult..

Why Location Matters - Population Surveillance

Why Location Matters - Population Surveillance

Than this..

Why Location Matters - Population Surveillance

Development Process

Management TeamLed by public health MD with informatics

trainingMembers from each area of DSP involved

User InvolvementUsers on management team Input throughout requirements, design,

development

Software Required and Our Choices

Software Type Required Our Choice

~GIS MapServer

General + Spatial DB PostgreSQL + PostGIS

Cartography-enabled client HTML/Javascript

Analytical / statistical tools SaTScan, R, Python

Web Architecture Benefits

Usually lighter/simpler technologiesCross-platformEase of deployment and integrationBuilds on existing set of conventions and

behaviours

System Architecture

Oracle DB

Oracle Forms

Current Case Management System

Web client

Bridge

{Python

R

SaTScan

{ Apache + PHP

MapServer + MapScript

PostgreSQL/PostGIS DB

Dracones

Client Side - UI

UI is 100% Javascript (ExtJS library)Future project: extract the map-

manipulation parts:Tile-based panningZoomingLayer activation

And releasing them under an OS license

Client Side - Functions

From the results of a query performed in the Oracle client, launch the application to visualize the results

Inspect those results by varying certain parameters

Launch external analysis tools

Server Side - MapServer

MapServer: OS tool that add geospatial content to web applications

Can be used as a CGIInterface with many programming

languagesWorks very closely with PostGIS

Server Side - MapServer

MapServer with Apache 2.2, using PHP5Linux and WindowsSince it's stateless, each interaction:

Build a map object from a base mapfileModify the map object (according to client

parameters)Return rendered map as a file to the client

(that will display it)

MapServer - Layers

A map object is made of layersA layer can be loaded from a shapefile

(ESRI open format), that specifies its geometry

Or it can be loaded directly from a PostGIS table

PostGIS

PostGIS: spatial extension for PostgreSQL

Adds geometry types (points, lines, polygons, etc)

Spatial functions and operators (distance, convex hull, intersection, etc)

Spatial indexes

PostGIS

Queries that mix spatial and non-spatial aspects of the data

If you have a case table:

case_id condition region_id

1 TB 10

2 Gastro 20

PostGIS

And a region table:

region_id name geom

10 Centre-Sud POLYGON(…)

20 Hochelaga POLYGON(…)

PostGIS

You can then build a query like this:

SELECT * FROM case, region

WHERE case.condition = 'TB'

AND case.region_id = region.id

AND within(region.geom,

GeomFromText('POLYGON(…)')

PostGIS

A MS layer can be built simply by adding a connection attribute, pointing to the PG table (two lines really!)

Shapefile and table sources can be mixed

Analysis Tools - SaTScan

Requirement: interfacing with analysis tools

SaTScan: detection of space-time clusters Scan for areas where the probability of

being a case is significantly higher than being a non-case

Analysis Tools

Since it's a command-line tool without an open API, we use Python to run it, parse the results and plot them using MapServer

We do the same for some external R routines

System Data Sources

Health dataReportable disease databaseAncillary data on contacts

Geographical dataStreet networks and postal code fileHealth regions, census, postal boundaries

Using Address Data from a Public Health Database

Problem: addresses are stored as character fields:

No validation at the entry pointData quality is compromised

Address:

1500-a Sherbroooke St. Ouest

Two Problems with Address Processing

The addresses need to be parsed, and possible (and numerous) transcript errors and ambiguities must be solved

The ones which refer to a same place must be identified and treated as a unique object

Possible Solutions

These could be solved in a more SQL-integrated manner: edit distance module for PG (?)

We decided however to go the procedural way (using Python)

Address Validation Algorithm - Requirements

A database with (1) the street network geometry

(2) the street segment address rangesAnd (3) the postal code geometry and

street range association

Address Validation Algorithm

So you will know for instance that:

Sherbrooke

StreetSherbrooke Street

1001

2001

3001998

1998

2998

H2X2T1H2X2T2

Address Validation Algorithm - Steps

Parse the text addresses in 3 tokens: {S#, SN, PC}

For each triplet:Try to find an exact match, by being tolerant

on SN (maximum coverage, edit distance..)By being tolerant on SN, try to vary PC Idem with SN, fix PC and vary S#

Address Validation Algorithm - Batch Results

By doing a batch analysis of the DSP data (105K records), we found that:84% of the address records were "exact"14.5% were recoverable errors1.5% were non-recoverable errors

Last Address Processing Step: Geocoding

Geocoding by interpolation:

Sherbrooke

StreetSherbrooke Street

1001

2001

3001998

1998

2998

H2X2T1H2X2T2

1500 Sherbrooke

A Last Problem

DSP management system is read-only (for us)

Not spatially enabledMust not affect performance

And its Solution

Create a mirror of the DSP data model, using PG

Augmented with spatial aspects (and more adapted address handling)

Refreshed periodicallyReprocessing of the content that has

changedExtraction of the new one

A Challenge

Interface and extend existing:SystemEnvironment (including an important

community of users and developers)

Lessons Learned

Very strong interest in using spatial information at the DSP but infrastructure, skills and data quality are limiting Large effort to validate and correct all addresses

The science of spatial analysis in public health often lags the technology How to analyze multiple locations for each individual? How important is spatial location in an urban area?

Open-source, web-based mapping software and spatial databases (MapServer, PostGIS) are robust and easy to work with for skilled developers

Acknowledgements

GeoConnections, CIHRMcGill University

Aman Verma, Sherry Olsen, Andrew CarterMontreal DSP

Louise MarcotteRobert Allard, Lucie Bedard, André Bilodeau

Montreal Chest InstituteKevin Schwartzman, Jonathan RichardAlice Zwerling, Marie-Josee Dion