CERC/montreal_dataset
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Montreal Building Data

Contributors

Kartikay Sharma (kartikay.sharma@concordia.ca)

Koa Wells (kekoa.wells@concordia.ca)

Dataset Description

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.

Data Sources

  1. Montreal Property Assesment Units
  2. Montreal 3D Buildings (LOD2 model with textures)
  3. Montreal Aerial Lidar Dataset
  4. NRCAN Building Footprints
  5. Administrative boundaries of the agglomeration of Montréal (boroughs and related cities)

List of dataset changes

  1. Removed redundant building footprint vertices
  2. Removed duplicate buildings

Known issues with dataset

Dataset Information

This dataset contains 3d building in multipatch format for 6 teritorries of Montreal city with their respecitve construction year and building use type.  The Administrative Boundaries are:  

  1. Anjou:
  2. Pointe-Claire:
  3. Rosemont-La Petite-Patrie:
  4. Kirkland:
  5. Westmount:
  6. Hampstead:
  7. Mercier-Hochelaga-Maisonneuve:
  8. Senneville:
  9. Le Sud-Ouest:
  10. Rivière-des-Prairies-Pointe-aux-Trembles:
  11. Sainte-Anne-de-Bellevue:
  12. Le Plateau-Mont-Royal:
  13. Verdun:
  14. Dollard-Des Ormeaux:
  15. Montréal-Est:
  16. Baie-D'Urfé:
  17. Lachine:
  18. Côte-des-Neiges-Notre-Dame-de-Grâce:
  19. Villeray-Saint-Michel-Parc-Extension:
  20. L'Île-Dorval:
  21. Côte-Saint-Luc:
  22. Beaconsfield:
  23. Pierrefonds-Roxboro:
  24. Montréal-Nord:
  25. Mont-Royal:
  26. Montréal-Ouest:
  27. Ahuntsic-Cartierville:
  28. Saint-Léonard:
  29. Outremont:
  30. Ville-Marie:
  31. L'Île-Bizard-Sainte-Geneviève:
  32. Dorval:
  33. Saint-Laurent:
  34. LaSalle:

Côte-des-Neiges–Notre-Dame-de-Grâce: 20100 buildings  Outremont:3529 buildings  Le Plateau-Mont-Royal:14326 buildings  Le Sud-Ouest: 9926 buildings  Verdun: 9001 buildings  Ville-Marie: 8371 buildings

Meta data

  1. "ID_UEV": unique building identifier
  2. "ANNEE_CONS": year of construction
  3. "CODE_UTILI": building usage type
  4. "ADMIN_BOUNDARY": adminstrative boundary the building belongs to

Yes, these file also contains meta data information available for understanding what different attributes means. the same can be accessed through:

  1. For 3D building multipatches: donnees.montreal.ca/ville-de-montreal/batiment-3d-2016-maquette-citygml-lod2-avec-textures2#territories
  2. For building use, and other semantic data: https://donnees.montreal.ca/ville-de-montreal/unites-evaluation-fonciere

 

If No, whether the user created one? Or is there any source to understand the data (if the info is available)

 

Any psuedonames are used to represent the data attribites? If so, info is available explaining this psuedonames are available?

 

Data available (mandatory)

Not applicable

 

For example:

 

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)

 

Weather related data - Solar radiaition (Unit), Outdoor Temperature (Unit), Wind Speed (Unit), etc.,...  (If possible information on whether weather parameters are measured from a local weather location or from indvidual homes could be helpful)

 

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

 

Gas measurement data

 

Hot water data, etc....

 

Missing values (mandatory)

 

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

 

Missing value format in the dataset: 'Na' or 'NaN' or '-99' or any other formats

 

Continously missing values or sparsely missing values or No missing values

 

Data Cleaning (If it is processed dataset, it is mandatory to provide this info)

Whether missing values are processed?  If so, what is the technique (Moving average, any machine learning techniques, etc...)

 

Any date/data is excluded because of continously missing values?

 

Data processing (If it is processed dataset, it is mandatory to provide this info)

Any information related to data aggregation (example: minutes to hour), data transformation (example: min-max, Z-transformation), outlier detection (example: inter-quartile range, etc..)

 

Supporting data (optional)

 

Any other information that can be useful to the new users. For example

 

There is one other zip file with supporting information in. This includes:

 

  • documents that detail measurements and data visualisation (one per house)
  • appliance details, images and inventory
  • document that describes the data processing methods and a zip file of the Octave functions used
  • document that details the measurement device calibration
  • floor plans, dimensions and PIR/DOR locations
  • house construction
  • house occupant details
  • installation data and meter readings
  • a spreadsheet that details all measurements taken on the project (used to create the device ID in the header files)

 

Alert/Caution (Mandatory)

Do's & Dont' of the dataset

 

Any inherent problem associated with the data, etc...