Datasets from the City of Toronto Open Data - Data Catalogue. Date: June 6, 2014 (Neighbourhoods) and December 17, 2014 (Demographics) Website: http://www.toronto.ca/demographics/neighbourhoods.htm and http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=4482904ade9ea410VgnVCM10000071d60f89RCRD Contact: Open Data Team, opendata@toronto.ca

These profiles were developed to help government and community service agencies with their local planning, by providing socio-economic data at a meaningful geographic area. Not all people define “neighbourhoods” the same way. For the purposes of statistical monitoring however, these neighbourhoods were defined based on Statistics Canada census tracts. Census tracts include several city blocks and have on average about 4,000 people. Most service agencies have service areas that are defined by main streets, former municipal boundaries, or natural boundaries such as rivers. These service areas include several census tracts. It is not uncommon for service areas of community agencies to overlap. Choices about neighbourhood boundaries were made to make the data in the profiles useful to as many users as possible, and are not intended to be statements or judgements about where a neighbourhood starts or ends. The boundaries for these neighbourhoods were developed using the following criteria:

1) originally based on a Urban Development Services Residential Communities map, based on planning areas in former municipalities, and existing Public Health neighbourhood planning areas;

2) no neighbourhood be comprised of a single census tract;

3) minimum neighbourhood population of at least 7,000-10,000;

4) where census tracts were combined to meet criteria 2 or 3 above, they were joined with the most similar adjacent area according to % of the population living in low income households;

5) respecting existing boundaries such as service boundaries of community agencies, natural boundaries (rivers), and man-made boundaries (streets, highways, railway tracks);

6) maintaining neighbourhood areas small enough for service organizations to combine them to fit within their service area; and

7) the final number of neighbourhood areas be “manageable” for the purposes of data presentation and reporting.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

Toronto Neighbourhoods Boundary File includes Crime Data by Neighbourhood. Counts are available at the offence and/or victim level for Assault, Auto Theft, Bike Theft, Break and Enter, Robbery, Theft Over, Homicide, Shootings and Theft from Motor Vehicle. Data also includes crime rates per 100,000 people by neighbourhood based on each year's Projected Population by Environics Analytics.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario..In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Dataset contains polygon (area) features.

Summary of the City of Toronto Zoning, summarized by neighbourhood. Courtesy of the City of Toronto Open Data team (opendata@toronto.ca) Zoning: http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=5a9923e69b4a6410VgnVCM10000071d60f89RCRD&vgnextchannel=1a66e03bb8d1e310VgnVCM10000071d60f89RCRD Neighbourhoods: http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=04b489fe9c18b210VgnVCM1000003dd60f89RCRD&vgnextchannel=1a66e03bb8d1e310VgnVCM10000071d60f89RCRD

This is a georeferenced raster image of a printed paper map of the Toronto, Ontario region (Sheet No. 030M11), published in 1960. It is the fourth edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1960 and the information on the map is current as of 1949. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

The data layer shows the sign district designations of all properties in the City of Toronto - the sign bylaw regulations/permissions/restrictions that are applicable to each property in the city are based on its sign district designation. The data is used in conjunction with a City of Toronto map so that individual properties can be searched by address or through a zoom tool. The city provides this data to the public through an online mapping and search tool at: https://map.toronto.ca/maps/map.jsp?app=SignView_2

The data layer shows the sign district designations of all properties in the City of Toronto - the sign bylaw regulations/permissions/restrictions that are applicable to each property in the city are based on its sign district designation. The data is used in conjunction with a City of Toronto map so that individual properties can be searched by address or through a zoom tool. The city provides this data to the public through an online mapping and search tool at: https://map.toronto.ca/maps/map.jsp?app=SignView_2

Toronto Red Light Cameras

Public dataset for the red light cameras in Toronto.

We could use the dataset to classify the red light cameras per neighborhood.

Is it true that poor neighborhoods have more police surveillance and therefore more red light cameras ?

We could also use this dataset with the Toronto map and use the longitude, latitude to place the cameras, we could use Artificial Intelligence algorithm in order to find the fastest path from one side of the city to another.

This dataset includes all Major Crime Indicators (MCI) occurrences by reported date and related offences since 2014.Major Crime Indicators DashboardDownload DocumentationThe Major Crime Indicators categories include Assault, Break and Enter, Auto Theft, Robbery and Theft Over (Excludes Sexual Violations). This data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

The dataset, titled "Environmentally Significant Areas," falls under the domain of Environment, Locations, and Mapping. It is tagged with keywords such as Housing Potential, land use, and official plan. The dataset is available in various formats including JSON, CSV, GeoPackage+SQLite3, and SHP. It was published on April 3, 2020, and the location details are provided within the dataset. The owner and author of the dataset is City Planning, with the contact email being sustainablecity@toronto.ca. The dataset is managed by the City of Toronto Open Data organization. The dataset provides information about the City's Environmentally Significant Areas as shown on Map 12A of the Official Plan. These areas are particularly sensitive and require additional protection to preserve their environmentally significant qualities. The source of the dataset is provided and it is licensed under the Open Government Licence – Toronto. The dataset includes various resources such as 'Environmentally Significant Areas - 4326.gpkg', 'Environmentally Significant Areas - 2945.gpkg', and 'Environmentally Significant Areas - 4326.csv' among others. The metadata for the dataset was created on October 4, 2024, and was last modified on April 9, 2025.

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

The City's Environmentally Significant Areas are shown on Map 12A of the Official Plan. Environmentally Significant Areas are areas of land or water within the natural heritage system that have characteristics which make them particularly sensitive and require additional protection to preserve their environmentally significant qualities. For more information about ESA's please visit the Environmentally Significant Areas information page.

This map is currently under development.The Transportation Tomorrow Survey (TTS) is a comprehensive travel survey conducted in the Greater Golden Horseshoe Area (GGHA) once every five years. Participants in the design and implementation of the survey include Cities of Toronto and Hamilton, the Regional Municipalities of Durham, Halton, Peel and York, GO Transit, the Toronto Transit Commission and the Ontario Ministry of Transportation.The survey collects information about households, demographics, and trips in an extremely detailed manner. In order to provide some summary data to the public, the University of Toronto's Data Management Group provides ward-level (Greater Toronto Hamilton Area only) and planning-district level counts (entire GGHA) for over 400 different categories. The list of downloadable open data tables can be found on the group's website. This map contains a number of layers which show ward-level counts for the Greater Toronto Hamilton Area data for a categorized subset of the 400+ categories available including:Household sizeNumber of trips to and from individual wards by mode in a 24 hour period.Number of trips to and from individual wards by purpose in a 24 hour period. *Not yet available*Fields with a value of 4 or less have been rounded to zero to preserve anonymity.

Subset of Toronto with StatCan's proximity indicators and enriched 2020 data from Environics. This map uses the Relationship symbolization for the following two variables:Health Proximity Measure2020 Median IncomeToronto Neighbourhoods were pulled from ArcGIS Online to display boundaries and neighbourhood names.

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: Price in dollars Address of the property Latitude and Longitude of the address obtained by using Google Geocoding service Area Name of the property obtained by using Google Geocoding service This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas) However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes). This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/ I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction