Newcomer Service Access: Census and Postal Code Mapping The Toronto South Local Immigration Partnership

Purpose of Project Census and National Household Survey mapping Mapping 2011 census information for the LIPs; mapping is City-wide These maps provide us with the most up-to-date demographic information for the City; will also help member agencies in program planning Note: given changes in data collection methods, NHS data is not the most reliable

The trend generally appears to be stable (some increasing, some decreasing)

While there are high income areas, some outliers While there are high income areas, some outliers. For the most part they are low to medium income

Inner suburbs

Clustering of particular language groups in areas (which when we look at it as a whole is an interesting finding) - Pretty much complete

Purpose of Project Service Postal Code Mapping This project mapped postal code data of clients from participating agencies; this gave us a sense of the demand for services The locations of these agencies was also mapped; this will give us a sense of the supply (services available for newcomers) Privacy issues Given we have not been given permission from participating agency to show their data that we have mapped, we will be showing made up data from a fake organization to demonstrate what kind of patterns could theoretically be identified using this technology. Given the patterns that we have mapped we can get a sense of behaviour but not necessarily preference The objective is to bring together supply and demand in an effective manner. (hospital has patients, school has students, agency has clients) Client behaviour is distinctively spatial (geographical) in nature

Results For Participating Agencies: Agencies received maps with their data, which gave them a sense of where clients are coming from to access services as well as trends in their area(s) of service We’ve received some feedback that this information has been helpful in program planning, evaluation and funder reporting for agencies who participated

Results For the LIP This information gave us a sense of trends with our partner agencies Also identified trends in data collection with our partner agencies – data collection differs substantially from agency to agency

South lip agency 5 locations

Clustered patterns is revealed Distance decay relationship between agency and client locations (a decreasing number of clients with increasing distance from the supply points) – which is really an economic consideration, since greater distances require greater costs (to put it another way, interaction with a location declines with distance) Some anomalies (proximity to a subway line, or access to a fast freeway could factor into the decision, or the location being near to their workplace) Partner with agencies in other areas (opporutnity to move to another location) Either to move to north york (or partner with another agency) – to situate service there, since there appears to be a need

Note the clustered FSA in downtown Toronto is actually less than the FSA close to the North York area, because the FSA size is smaller, but more dots in a smaller area may give you a wrong appearance. Actually most clients come from the North York region (3 FSA)

Coordination of programs/services can be coordinated at different geographical levels. 15 minutes 30 minutes 1 hour Here, drive time service areas were generated around each supply point utilizing the Street Network. These areas estimate the travel time around all 5 supply points. Specific threshold values were attained using drive times from each location. Drive times– which is the only available measure in GIS

Limitations & Next Steps When we received data from organizations, we found that this data was limited and very different from agency to agency (every agency collects their data differently) Next Steps: We hope to have discussions about data collection and discuss with partners potential benefits to standardized client data collection (potential pilot project)

Possible Best Practice Ricky identified one program that he mapped that was a potential best practice in client data collection

This kind of detailed mapping was made possible because this organization tracked their clients based on location This was not typical of the majority of the organizations that Ricky mapped

This kind of detailed mapping was made possible because this organization tracked their clients based on country of origin This was not typical of the majority of the organizations that Ricky mapped

This kind of detailed mapping was made possible because this organization tracked their clients based on the year clients entered Canada This was not typical of the majority of the organizations that Ricky mapped

Because of the detail of data collection, we are actually able to identify how far clients travelled for a particular service The assumption is that clients travel to the closest location in order to receive services, but this map shows that this is not always the case