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The purpose of this repo is to ensure that everyone has access to the latest version of the Montreal city dataset.
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# Montreal Building Data
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## Contributors
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Kartikay Sharma (kartikay.sharma@concordia.ca)
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Koa Wells (kekoa.wells@concordia.ca)
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## Dataset Description
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The following dataset contains the collection of buildings in the geojson format for the city of Montreal. The dataset is created by combining multiple open-source datasets provided from the Canadian federal government and the Montreal city government. The dataset is split into several different files. The first file, _name_ contains the entire dataset for the buildings of Montreal. The remainig files contain the buildings sorted by admistrative boundary/district.
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## Data Sources
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1. [Montreal Property Assesment Units](https://donnees.montreal.ca/dataset/unites-evaluation-fonciere)
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2. [Montreal 3D Buildings (LOD2 model with textures)](https://donnees.montreal.ca/dataset/batiment-3d-2016-maquette-citygml-lod2-avec-textures2)
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3. [Montreal Aerial Lidar Dataset](https://donnees.montreal.ca/dataset/lidar-aerien-2015)
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4. [NRCAN Building Footprints](https://open.canada.ca/data/en/dataset/7a5cda52-c7df-427f-9ced-26f19a8a64d6)
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5. [Administrative boundaries of the agglomeration of Montréal (boroughs and related cities)](https://donnees.montreal.ca/dataset/limites-administratives-agglomeration)
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## List of dataset changes
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1. Removed redundant building footprint vertices
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2. Removed duplicate buildings
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3.
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4.
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5.
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6.
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7.
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8.
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## Known issues with dataset
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## Dataset Information
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This dataset contains 3d building in multipatch format for 6 teritorries of Montreal city with their respecitve construction year and building use type.
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The Administrative Boundaries are:
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1. Anjou:
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2. Pointe-Claire:
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3. Rosemont-La Petite-Patrie:
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4. Kirkland:
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5. Westmount:
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6. Hampstead:
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7. Mercier-Hochelaga-Maisonneuve:
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8. Senneville:
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9. Le Sud-Ouest:
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10. Rivière-des-Prairies-Pointe-aux-Trembles:
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11. Sainte-Anne-de-Bellevue:
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12. Le Plateau-Mont-Royal:
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13. Verdun:
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14. Dollard-Des Ormeaux:
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15. Montréal-Est:
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16. Baie-D'Urfé:
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17. Lachine:
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18. Côte-des-Neiges-Notre-Dame-de-Grâce:
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19. Villeray-Saint-Michel-Parc-Extension:
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20. L'Île-Dorval:
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21. Côte-Saint-Luc:
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22. Beaconsfield:
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23. Pierrefonds-Roxboro:
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24. Montréal-Nord:
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25. Mont-Royal:
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26. Montréal-Ouest:
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27. Ahuntsic-Cartierville:
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28. Saint-Léonard:
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29. Outremont:
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30. Ville-Marie:
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31. L'Île-Bizard-Sainte-Geneviève:
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32. Dorval:
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33. Saint-Laurent:
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34. LaSalle:
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Côte-des-Neiges–Notre-Dame-de-Grâce: 20100 buildings
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Outremont:3529 buildings
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Le Plateau-Mont-Royal:14326 buildings
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Le Sud-Ouest: 9926 buildings
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Verdun: 9001 buildings
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Ville-Marie: 8371 buildings
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## Meta data
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1. "ID_UEV": unique building identifier
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2. "ANNEE_CONS": year of construction
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3. "CODE_UTILI": building usage type
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4. "ADMIN_BOUNDARY": adminstrative boundary the building belongs to
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Yes, these file also contains meta data information available for understanding what different attributes means. the same can be accessed through:
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1. For 3D building multipatches: donnees.montreal.ca/ville-de-montreal/batiment-3d-2016-maquette-citygml-lod2-avec-textures2#territories
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2. For building use, and other semantic data: https://donnees.montreal.ca/ville-de-montreal/unites-evaluation-fonciere
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If No, whether the user created one? Or is there any source to understand the data (if the info is available)
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Any psuedonames are used to represent the data attribites? If so, info is available explaining this psuedonames are available?
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***********************************
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Data available (mandatory)
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***********************************
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Not applicable
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For example:
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Time stamp: Unix/Epoch time. Date format: MM/DD/YYYY HH:MM or YYYY-MM-DD HH:MM:SS or..... (This is an important info for processing the dataset later)
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Weather related data - Solar radiaition (Unit), Outdoor Temperature (Unit), Wind Speed (Unit), etc.,...
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(If possible information on whether weather parameters are measured from a local weather location or from indvidual homes could be helpful)
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Energy related data - For example Plug load (Unit), Lighting Load (Unit), HVAC (Unit). Please provide whether the data available is the total energy data or sub-metered data
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Gas measurement data
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Hot water data, etc....
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***********************************
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Missing values (mandatory)
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***********************************
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Yes, these dataset contiains many missing values which are currently empty cells, but work is going on to either remove the empty columns or change them with NA
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Missing value format in the dataset: 'Na' or 'NaN' or '-99' or any other formats
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Continously missing values or sparsely missing values or No missing values
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***********************************
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Data Cleaning (If it is processed dataset, it is mandatory to provide this info)
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***********************************
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Whether missing values are processed?
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If so, what is the technique (Moving average, any machine learning techniques, etc...)
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Any date/data is excluded because of continously missing values?
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***********************************
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Data processing (If it is processed dataset, it is mandatory to provide this info)
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***********************************
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Any information related to data aggregation (example: minutes to hour), data transformation (example: min-max, Z-transformation), outlier detection (example: inter-quartile range, etc..)
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***********************************
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Supporting data (optional)
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***********************************
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Any other information that can be useful to the new users. For example
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There is one other zip file with supporting information in. This includes:
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- documents that detail measurements and data visualisation (one per house)
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- appliance details, images and inventory
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- document that describes the data processing methods and a zip file of the Octave functions used
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- document that details the measurement device calibration
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- floor plans, dimensions and PIR/DOR locations
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- house construction
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- house occupant details
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- installation data and meter readings
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- a spreadsheet that details all measurements taken on the project (used to create the device ID in the header files)
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**********************************
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Alert/Caution (Mandatory)
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**********************************
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Do's & Dont' of the dataset
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Any inherent problem associated with the data, etc...
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