BC Public Lands Map Methodology

Joining suitable parcels with other data layers to rate suitability for housing

We made use of a range of other data layers sourced from Statistics Canada, BC Data Catalogue, Native Land Digital, Housing Infrastructure and Communities Canada (HICC), the Integrated Cadastral Information Society, Geo.Ca, Natural Resources Canada, DMTI Spatial and LidarBC to create a detailed multidimensional profile for each parcel. In some cases, data processing was required before joining to the public land data. These layers and processes are discussed by general category type below.

Proximity to amenities

We used HICC’s Spatial Access Mapping (SAM) products to rate proximity to employment, K-12 and post-secondary education facilities, health and childcare services, cultural and fitness centres and grocery stores, based on walking distance and distance using public transit. DBs with NULL values in the SAM data were deemed to be located within water features with zero access to amenities. Approximately 93 publicly-owned parcels were located in DBs with NULL SAM values, and were deleted. SAM does not consider proximity to pharmacies so we calculated straight-line distances from parcel boundaries to pharmacies separately using BC Data Catalogue’s pharmacy spatial layer. We also calculated straight line distances from parcel boundaries to parks and transit stops.

Infrastructure

We used data sourced from the Integrated Cadastral Information Society (ICI Society) to calculate minimum straight-line distances from parcel boundaries to primary electrical wires and water mains. We estimate that approximately 2/3 of municipalities in BC share infrastructure data with ICI Society, with many municipalities providing partial or no infrastructure data. We conducted tests of means test to identify municipalities with mean distances to infrastructure well above the provincial mean, and deemed those municipalities as having no data for the respective infrastructure data. We also used the Digital Road Atlas from GeoBC to calculate minimum straight-line distance to the nearest road.

At the outset of this project, we intended to capture infrastructure capacity in addition to access since this information can be useful when considering development viability, as system upgrades can add a significant cost burden to a project. However, after reviewing the available data sources, we’ve determined that there is not a reliable data source that reports capacity consistently across jurisdictions.

We considered Canada’s Core Public Infrastructure Survey (CCPI). This survey has data on the length, quality, and age of a variety of infrastructure. However, these values are aggregated at the census subdivision level, and the survey does not provide any indication of how close to maximum capacity these infrastructure assets are, nor is there a clearly-delineated rubric to measure these assessments across jurisdictions.

Natural Features and Hazards

1. Flood risk was determined through the use of the Flood susceptibility index from Geo.ca. Flood risk in the Flood susceptibility index is determined down to areas of 30 square metres. Although the Flood susceptibility index is not recommended for street-level risk analysis, it was deemed appropriate to indicate in the BCPLM when a parcel fell within a moderate to very high flood risk area. The BC Provincial Government continues to produce updated local area flood maps, and users should refer to up-to-date local area floodplain maps to confirm flood risks for specific parcels.
2. Wildfire risk for parcels was determined using BC Wildfire PSTA Fire Threat Rating data from the BC Data Catalogue. Each parcel was assigned the highest Wildfire risk value within 200m of a parcel’s centroid.
3. We also considered proximity to riparian features using DataBC’s Fresh Water Atlas, deeming a parcel to be in a riparian area if any part of it fell within a 50m buffer of a freshwater feature.

Site Conditions

High resolution (1m-2m pixel) digital elevation model tiles from LidarBC and the federal government were mosaiced together and used to create a slope layer to calculate mean slope for each parcel. We overlaid point data from Environmental Remediation Sites in the BC Data Catalogue, which contains data on known and potentially contaminated properties in British Columbia, for sites that have been screened, investigated or cleaned up since data collection began.

Please note that an environment remediation flag is neither an indication that a site will, or will not, require remediation, it simply indicates that a site was flagged for remediation at some time. There is currently no reliable source of province-wide data on sites with current remediation concerns nor the extent of those concerns.

We overlaid soil data from the Soil Survey Spatial View in the BC Data Catalogue to rate soil drainage.

Zoning, Official Community Plans, Development Areas, Historical Sites, Dwelling Density and Recent Construction

1. Zoning and Official Community Plan (OCP) data from ICI Society, where available, were determined for each parcel.²
2. We calculated BC transit-oriented development areas as per BC’s Bill 47 and Major Transit Growth Areas and Urban Centres from Metro Vancouver’s open data portal.
3. We used a Data BC Heritage features layer to identify if parcels had heritage values. To characterize the current development context of parcels, Statistics Canada “dwellings per Dissemination Block (DB)” data were used to determine dwellings per acre density for each DB. This was then used to determine maximum and median dwellings per acre density in a 1 km radius around each DB to create a measure of existing, median and maximum density in a standard neighbourhood around each parcel.
4. We used the BCA Residential and Non-residential Inventory tables to identify parcels with buildings constructed between 2020 and 2025 to create an index of recent builds across the province and an indicator of local building activity.
5. Finally, we overlaid spatial data from the Native Land Map from Native Land Digital (native-land.ca) to include all First Nations Territories listed in the Native Land Map which overlapped in part or in whole with every parcel in the BCPLM. Please note that this information is intended to support an expansive, high-level understanding of parcels. None of the First Nations Territory as reported in the Native Land Map has been edited.

See the following table for more information about each indicator used. Each RD public land layer was spatially joined to these indicator layers or the distance to the indicator feature (for instance transit stop, roads, pharmacies) was calculated.

2. Some jurisdictions across the province do not have open data policies or data-sharing agreements with ICI Society, so some features and data are not possible to include in the BCPLM as a result.

Processing data for the online map

The data are gathered into a geodatabase. In ArcGIS Pro, new fields are created to prepare the data for the end user. The resulting data is turned into two feature layers, one with polygons and one with points derived from the centroid of the parcels, for use in the online map. These two feature layers are added to an ArcGIS Web Map. Within the web map we format the fields, generate the map symbology, and format the pop-up window. The ultimate map accessed by the user is in ArcGIS Experience Builder where the filters and toggles are created.

Additional fields created:

1. Parcel shape: To assess whether a parcel has an irregular shape that may make it more challenging to build upon, we classify each parcel as either “Regular” or “Irregular”.³

1. a. First, we estimate the parcel’s width (shortest side) based on the diameter of the largest circle that fits within the parcel and estimate the length (longer side) as the parcel’s area divided by estimated width.
2. b. Then, we calculate how closely the shape resembles a perfect rectangle. We know the actual area and perimeter of each parcel, so we can calculate what the area would be using our estimated width and a different estimate of length based on the actual perimeter (Implied Area = Width * [Perimeter – 2*Width]/2).
3. c. Then we can divide Implied Area (assuming the shape is rectangular) by Actual Area to see how close the shape is to a perfect rectangle. If the ratio equals 1, then the parcel is a rectangle. If the ratio is less than 0.9 or greater than 1.1 then we conclude that the shape has significant deviations from a rectangle and is irregularly-shaped.
4. d. We also calculate the aspect ratio of each parcel as the estimated length divided by estimated width. If the length is more than 4 times longer than the width, then we conclude it is irregularly-shaped even if it’s rectangular.

2. Access to amenities (by RD)

• a. Most amenities from the SAM dataset are measured as a value between 0 and 1 that has been standardized across Canada so that 1 represents the dissemination block with the greatest access, and 0 means no reasonable access by a road network. We calculate the terciles for these values within each RD – if a parcel is in the uppermost tercile then it has Excellent access; if in the second tercile is has Good access; if in the first tercile is has Poor access; if the value is 0 is has No reasonable access via a road network.
  1. i. Grocery stores are in the SAM dataset but are measured as the number of minutes to the first nearest grocery store, as well as the third-nearest, and the fifth-nearest. If the nearest grocery store is within 10 minutes we rated it as having Excellent access; if 10-20 minutes, Good access; if 20-30 minutes, Poor access; and if greater than 30 minutes or null values then No access.
  2. ii. SAM data exists for walking, biking, and transit. We apply the best measure of access by walking or by public transit as the parcel’s SAM value for a given amenity.
• b. Access to Pharmacies, and transit stations / bus stops, are calculated as the distance in meters to the nearest amenity. We assume that the average person can walk 720 meters in 10 minutes, so we use that assumption to apply a similar categorization as with grocery stores: if distance is under 740m (approx. 10 mins walking) then Excellent access; if 720-1,440m (approx. 10-20 mins walking) then Good access; if 1,440m-2,160m then Poor access; and if distance is greater than 2,160m, No access.

3. Recent Development Index

• a. Drawing from BCA Data, we were able to estimate spatially the number of dwellings completed in the years 2021-2025. Every entry in the 2025 Residential Inventory was allocated to spatial data parcel by the UBC Library. By summing every dwelling unit with a Year Built of 2021-2025, we mapped the location of residential completions as points in each RD.
• b. A Kernel Density (Spatial Analyst) function was applied to the point data for completions 2021-2025, to convert the points to create a spatial gradient of development.
• c. In order to make the recent development index sensitive to urban versus rural/northern contexts, where the scale of development varies significantly, we converted the nominal values for residential completions into a 5-category index for each RD: Very Low, Low, Moderate, High, or Very High. 
• d. These categories for each RD are calculated using the “Natural/Jenks Breaks” classification method. Natural Breaks is a statistical data clustering method that groups similar values together by identifying natural “peaks” and “valleys” in data distribution. We applied four break points define the range of nominal completions values for each of the five categories, and parcels are assigned to a category based on the spatial index value in which their centroid is located.

4. Housing density (units per acre) and potential housing yield (units per parcel)

• a. Housing density is calculated as the number of Total Dwellings (from the 2021 Census) divided by the land area for every DB in British Columbia. DBs vary greatly in size, as they are set when a piece of land is surrounded by roads on all sides. As a result, some DBs are very small parcels with a single high-rise tower, and they return an unrealistically high value of units per acre, so we top-coded every DB with a maximum value of 250 units per acre. This gives us a units per acre measure of housing density for every DB in BC. In the following sections we will refer to units per acre as values of density.
• b. We calculate three values of density for each parcel based on possible scenarios, plus an override if the parcel is in a provincially designated Transit-Oriented Area (TOA):
  • i. Current density: this is the density of the DB that the parcel’s centroid is within.
  • ii. Median neighbourhood density: this is the median density of all DBs with a centroid within a 1 km radius of the current DB’s centroid. If the median density value is lower than the current density, the current density value is applied.
  • iii. Highest neighbourhood density: this is the highest density that is reported for a DB with a centroid within 1 km of the current DB’s centroid. For example, a DB with a value current density value of 250 units per acre will have a current, median and highest neighbourhood density of 250 units per acre.
  • iv. TOA density: if the parcel is in a TOA tier, then the Highest density is calculated as the units per acre possible using the TOA-specific floor-to-surface ratio (source), and assume that 82.5% of the building space can be used for housing, and assuming that the average unit takes up 65 square meters. The max of 250 units per acre is applied here. TOA density overrides all other densities.
  • v. Finally, if a density value is calculated between 116 and 139 units per acre, the density is reduced to 115 units per acre. This accounts for a “dead zone” of certain structural types not being economically feasible within this density range. This typically applies when the mode of construction has to shift from wood-frame to concrete, but the achievable density is not high enough to offset the significantly higher construction costs of the move to concrete. In BC, this typically applies to a building which has between 7 and 11 residential storeys.
• c. Potential housing yield for a given parcel is calculated for each of the above three scenarios of current, median and highest neighbourhood density. The resulting numbers of units are a result of multiplying the parcel’s area in acres, by the number of units per acre in each of the three development scenarios.

5. Vacant and Underutilized

• a. A parcel is defined as vacant if the assessed improvement value for the parcel is zero, indicating that there are no structures present, or that the value of any structures on site are insignificant.
• b. A parcel is defined as underutilized if the assessed improvement value (i.e. the value of structures on a parcel) is less than 20% of the assessed land value. This is an arbitrary threshold to indicate where there may be a significant value gap between the land and what is built on it. The value ratio of improvements to land is reported for every parcel, so that users can still see this ratio even when the improvement/land value exceeds the 20% threshold.

6. Parcel Scoring

• a. Each parcel is given a BCPLM score out of a maximum of 100 points. The weighting of these points was validated by development consultants active in the BC non-market housing sector.
• b. Parcel scoring details are included below. The decisions around scoring and weights for various characteristics were made by the HART team with some consulting support from development consultants in BC.

3. We only apply this assessment to parcels under 5,000 square meters since larger parcels would be less constrained by setbacks, lot coverage, and other planning codes.

Critical Amenities

Normal Amenities

Potential Density

Recent Development

Vacant & Underutilized

Site Characteristics – Slope

Site Characteristics – Flood Risk

Site Characteristics – Shape

Site Characteristics – Fire Risk

Output fields – all categories

Assembling the BCPLM online

The BCPLM is displayed online using a license from ArcGIS. This section outlines the construction of the map in the infrastructure of the ArcGIS Web Map, Experience Builder, and Storymap.

1. Establishing the underlying data for ArcGIS
   1. a. Once the underlying data has been processed for each RD, they are combined into a single merged layer in ArcGIS Pro with every single publicly-owned parcel as a polygon.
   2. b. At this point we create around 50 new fields. Many fields are simply cleaned or formatted versions of the original data to look nice in the final map. Amenity categories are calculated, density and yield estimates, as well as the parcel shape estimates.
   3. c. A new layer is created with the same data but with points instead of polygons in order to generate the clustering functionality when the map is zoomed out.
   4. d. These two layers – the polygon and the point – are uploaded to ArcGIS online as feature layers.
   5. e. These two feature layers are used to populate the ArcGIS Web Map. This Web Map is where the parcel symbology, and clustering when zoomed out, is determined. This is also where the pop-up box for each parcel is configured.

2. Creating Graphs within ArcGIS
   1. a. A chart widget was added to the Web Experience to allow users to visualize and summarise data from parcels currently displayed in the Web Experience.
   2. b. We included widgets to display horizontal bar graphs of the following variables from the Web Map to show changes to results when filtering using the Web Experience:
      1. i. Number of parcels by Owner Type
      2. ii. Total area of parcels in metres squared by owner type
      3. iii. Number of parcels which are Vacant, Underutilized, or neither Vacant nor Underutilized
      4. iv. Total area of parcels in metres squared by Vacant, Underutilized, or neither Vacant nor Underutilized
      5. v. Potential housing yield according to the three development scenarios of Current, Median, or Highest density
   3. c. Finally, we added a static table for all of BC showing the Total Housing Yield by Density Scenario which disaggregates potential housing units by development scenario and built form.

3. Adding the The Business Analyst features (NOT produced by HART)

The Business Analyst widget in allows users to visualize key demographic, economic, and market indicators for any specific location or boundary in Canada using interactive infographics and tabular reports. The data is provided by Esri and more information can be found here.