Finding Your Ideal Toronto Neighbourhood

By Marian Mendoza
Geovisualization Project, @RyersonGeo, SA8905, Fall 2020

Project link: here. Best viewed in full screen.


Toronto is a diverse, exciting city with plenty to offer. When moving to a new city or finding a new neighbourhood to live in, it can be challenging to decide on what neighbourhood is best for you. This tool helps users filter their desired criteria based on a number of selected features.

Full screen view of the web app


Data for this project is collected from the following sources:

  • City of Toronto Open Data Portal for neighbourhood and green space shapefiles
  • for Walk, transit, and bike scores from
  • Metrolinx for subway stations (filtered from shapefile of Regional Transit Network).
  • Google Places API for museum/gallery, and mall (major shopping centres and district malls)


1. Collecting Google Places API data.
Querying the Google Places API returns a maximum of 60 results (as per Google’s Terms and Conditions). This query returns a list of results with extra features in a .json format. The desired results, specifically the name, point location, and address, are then reformatted into a usable .csv to be used in ArcGIS.

2. Data cleaning
All files were cleaned to retain only relevant fields for this geovisualization. Some neighbourhood names had changed since the data was published, so this was cross referenced with the City of Toronto’s current neighbourhood names. Further, Google Places API returned some misclassified or duplicated results that had to be removed from the list.

3. Map preparation on ArcGIS Pro.
– Walk/bike/transit scores were joined to the neighbourhood shapefile using the common area name.
– All other features were counted in each neighbourhood using “Summarize Within.” – For green_space, this returned the total area of green space in a neighbourhood. This was then computed as a percentage of the total area, producing the proportion of green space for neighbourhood. Next, the proportions were classified into 6 quantiles of “green levels” in the city.

4. Creating Widgets on ArcGIS Online Web App Builder + Using the Web App

All layers were uploaded to ArcGIS online and used as the web map for the web app builder. Several widgets were created to enable a user-interactive experience. Users are welcomed by a splash screen that explains how to use the app’s key functions.

FILTER. Since all features were joined to the neighbourhood layer, I created a filter widget that allows users to input values for any of the features. Most of these queries are set to “at least” since people who want higher values would be more selective. People who are not as particular about a feature can keep it at a low setting.

Setting the filter criteria

QUERIES. Several pre-set queries allow the user to see neighbourhoods with the top features in each category. The user can also engage further with the map layers by toggling on/off additional relevant features. For example, by querying “top transit scores” the user can then turn on the SUBWAY STATION layer and see where stations are in the city. They can also turn on “transit score” and see a choropleth map of transit scores across the city. This enables a richer understanding of the results of the queries.

Query for neighbourhoods with top transit scores, in descending order

CHARTS. The charts widget enables users to see a graphical representation of some of the features (scores and green space) and compare neighbourhoods.

Users have the option to use a spatial filter to limit the chart display to only some neighbourhoods. In this case, I zoomed into southwestern Toronto. The result is a bar graph where you can quickly compare the values for neighbourhoods in the set extent.

Chart comparing green space % in southwestern Toronto

Additional features in the web app:

SEARCH. Users can search for a place or an address and engage with any of the layers to see the features of that point’s neighbourhood.  

TRANSPARENCY. All layers have modifiable transparency. Users can layer several features and choropleth maps and identify neighbourhoods that may be most ideal based on the polygon’s saturation. For example, layering both walk score and transit score would show the darkest areas to be the most walkable and transit friendly.

ATTRIBUTE TABLE. All layers’ attribute tables are viewable for a user who would be interested in seeing the full details of each layer and use functions like “sort descending.” This is accessible in the “more details” menu for each layer, as well as the black tab in the bottom centre of the screen.


Originally, real estate data (such as rental prices) were to be included, but open data from the Canada Mortgage and Housing Corporation were not clean and complete for each Toronto neighbourhood. Additionally, restaurant, café, schools, and libraries were to be included, as these are some attractive neighbourhood features. However, due to Google Places API restriction of 60 requests, I decided to use smaller data. Alternatively, I could have used Nominatim API to pull more search requests from OpenStreetMap, but with time constraints I kept the scope of the project small.

There are limitations in using the widgets of ArcGIS Web AppBuilder. I would have liked the queries to display the “top results” with the colours assigned from the choropleth source layer. Instead, all results of a query are displayed with the same symbology. There is no option to group layers on ArcGIS Online; ideally, there would be groups for “feature layers” (subway station, mall, green space) and “classified layers” (walk score, green space %) that would help the user navigate the layers more simply.

Further development

Halfway through completing this project, I learned that Toronto Life magazine had created a similar tool. However, their tool scores each neighbourhood on an index and is interactive by using sliders to input the users ranking. The Toronto Life tool  does not allow the user to see the details of the index score for each neighbourhood, such as, identifying the types and locations of shopping experiences in the neighbourhood.

The Toronto Life tool gave me ideas on how to improve my tool. With more time and experience, I would create the app using Javascript to avoid the limitations of ArcGIS Online’s widget functionalities. I would expand on the filter function and allow the user to weight each feature, then return the results ranked by the best neighbourhood based on the criteria. Further, additional neighbourhood qualities not included in this project, such as housing affordability, building types, restaurants, and job opportunities, are complex datasets that would improve the comprehensiveness of this tool. I would use open data from OpenStreetMap to include features with more than 60 records. I would also improve the complexity of each feature’s relationship with other features (such as weighing a feature’s attractiveness to a neighbourhood by assessing its walking/transit/driving distance).

With more resources and access to clean and complete datasets, this tool can be expanded for broader use. Casual users can benefit from this tool, but it can also be used more precisely to complement real estate or housing research.