Base Parcel Canvas Creation
rev. April 2021
UrbanFootprint scenarios are built on the Base Canvas, a geospatial dataset that describes the existing environment. This detailed “canvas” of data constitutes a baseline assessment of land use, demographic characteristics, and other conditions, providing the context for scenario painting and a foundation for analysis using UrbanFootprint's modules.‌
When you create a new project, a Base Canvas is generated for your project area. Depending on your objectives, you can use a parcel canvas or a census block canvas. Parcel canvases depict development in terms of Building Types, and yield analysis outputs at the parcel scale. The resolution of parcel canvases is generally suited for detailed work up to the city level. Census block canvases (also referred to simply as "block canvases") use Census 2010 block geometries, and depict development in terms of broader Place Types. Scenario development and analysis at the census block scale is coarser. Generally, block canvases are suitable for work from the city level on up.‌
This documentation describes the data and process used to create the version of the parcel-scale Base Canvas used by default for new projects in the United States. Note that UrbanFootprint can incorporate local land use data as part of a custom parcel canvas creation process; for more information about this option please contact us.‌

Data Sources and Updates

The Base Canvas creation process incorporates data from a variety of sources, including:‌
  • Census data, used to allocate population, households, dwelling units, and employment
  • CoreLogic parcel data, used to identify land uses and populate dwelling units, employment, and
    building attributes where available
  • Point-of-interest data from supplemental sources, used for further identification of land uses
  • Road data, used for calculating intersection density
The methodology sections describe in detail how the source datasets are used. Note that the default Base Canvas is updated quarterly to incorporate the latest parcel data releases. For information about the updates, please see the latest release notes.‌

Methodology Overview

Base Canvas creation involves many steps of data processing and logic application. The process can be summarized into the following stages.‌
The key steps and data used in each stage are described in the sections that follow.‌

Standardizing Geometries

The Base Canvas is comprised of unique, non-overlapping feature geometries. UrbanFootprint uses quarterly-updated parcel data from CoreLogic, a leading provider of real estate data in the United States, which sources its data from tax assessors and county recorder offices.‌
Before the parcel data can be used, it needs to be cleaned to resolve any geometry issues. Common issues include duplicate IDs, stacked geometries, nested geometries, and overlapping geometries. As a post process, very tiny geometries (less than 1 square meter) are dropped.‌

Duplicate IDs

In the rare cases where there are duplicate parcel IDs, the first parcel is retained and the duplicates are dropped. A verification step further ensures that duplicate parcel IDs do not exist in the dataset.‌

Exact and Near-Duplicate Geometries

In some cases, such as parcels for condominiums, multiple geometries that are exactly the same or nearly the same are stacked on top of each other. These geometries are dissolved into a single flat geometry. For numeric data, the maximum across all parcel attributes is retained. For non-numeric data, the attributes of the parcel with the lowest parcel ID are retained.​‌
Figure 1. Representative example of duplicate geometry handling

Nested Geometries

Nested geometries sometimes occur in master-planned developments or subdivided residential neighborhoods where the outdoor area is owned by one entity, but each unit is privately owned. In these cases, a parent geometry (e.g., a planned unit development parcel) can have several child geometries (e.g., the planned units) as individual parcels completely contained by the larger parent geometry. For scenario development to correctly account for the total land area in these cases, it is essential that no land is double-counted, meaning that the parent geometry does not include the area of the child parcels. To fix this, the child geometries are cut out of the parent geometry. The result is that the parent geometry encompasses the shared common area, while each child parcel contains the information for the particular unit.​‌
Figure 2. Representative example of nested geometry handling

Overlapping Geometries

Finally, there are parcels that overlap but are not entirely contained in one or the other. This happens most often with condominiums where the floor plans of each unit differ across floors. For this type of parcelization to work in UrbanFootprint, it is important that no geometries overlap, even if they represent different floors in a structure. For these cases, the lower parcel ID has its geometry cut out and the overlapping portion is retained for the parcel with the higher parcel ID. The parcel area is recalculated for each new geometry, but each geometry retains its original attribute data, meaning that the unit number and building area are not impacted by the change in geometry.​‌
Figure 3. Representative example of overlapping geometry handling

Translating Land Use Codes

The next stage in the parcel canvas creation process is to assign UrbanFootprint land uses to parcels. As a first step, the original CoreLogic land use codes are "crosswalked" to a set of generalized land use designations to facilitate the process of typing parcels with UrbanFootprint's Building Types and higher levels of land use categories (see Land Use Hierarchy for more information). The land use typing is then further refined using supplemental datasets for specific land uses and points of interest.‌

Crosswalking to General Land Use Designations

Most jurisdictions represent land use at the parcel scale, using codes that reflect specific uses. UrbanFootprint “translates” this local information to represent development in terms of UrbanFootprint's standardized Building Types, which are foundational to scenario development and analysis. While land use and urban form are the subject of both local/regional land use classification systems and UrbanFootprint land use types, they are distinct languages. Land use codes are predominantly use-based and static, whereas UrbanFootprint's land use types (including Building Types at the parcel scale or Place Types at the census block scale, and the higher-level generalized categories into which they are classified) are primarily form-based. UrbanFootprint land use types are designed to be dynamic and expansive to capture the many variants of built form and land use. To relate UrbanFootprint Building and Place Types to the universe of local land use codes (as represented by CoreLogic's set of nearly 300 codes), we developed the Generalized Land Use Classification (GLUC) system.‌
The GLUC system is comprised of approximately 100 general land use designations. A crosswalk is used to associate each land use code from the CoreLogic parcel data with one of these general land use designations, each of which is associated with one or more UrbanFootprint Building Types. As part of Base Canvas creation, a translation algorithm uses this crosswalk to narrow the range of Building Types to which each CoreLogic land use code can be translated. In turn, residential and employment densities, as calculated in later steps, are used to select the closest fitting Building Type.‌
For example, CoreLogic’s "APARTMENT" land use code (#106) is crosswalked to the general land use designation "residential multifamily - all." A multifamily Building Type will then be selected based on the calculated residential density of the parcel. By contrast, CoreLogic’s "HIGH RISE CONDO" code (#117) is crosswalked to the "residential multifamily - high" designation, which is associated with a more restrictive set of potential Building Types.‌
The crosswalk between CoreLogic land use codes and UrbanFootprint land use are available upon request.‌ (Please contact UrbanFootprint Support through the in-app chat, or by email.)

Source-Specific Modifications

In some cases, CoreLogic commercial-based land use codes are incorrectly assigned to parcels that are not commercial but are held by a commercial entity (for example, a private developer). In such cases, we first check if the parcel contains a building or not. If the parcel does not contain a building, we invalidate the “commercial” land use assigned by CoreLogic and use supplemental datasets to inform the land use code.‌

Supplemental Datasets

Supplemental datasets provide more specificity where CoreLogic’s land use data may be lacking. UrbanFootprint uses a number of additional datasets for locating land uses and points of interest as summarized in Table 1.‌
UrbanFootprint tags parcels with a land use from CoreLogic and land uses from these supplemental datasets where they apply. The land use for a parcel is then “resolved” by picking one land use from this set of options. The land use that is most in agreement from the set of options is given priority, unless there is an exception-based rule. For example, between a choice of one “Industrial - All”, two “Commercial - All”s and vs. one “Commercial - Office,” the “Commercial - Office” Land Use is chosen since the parcel is most likely a “commercial” parcel with one land use specifically calling it a “Commercial - Office”.‌
Supplemental datasets can contain either polygon or point features. With polygon datasets, at least 25% of a parcel must be covered by the dataset to be tagged. With point datasets, a parcel is tagged directly if a point intersects it. If a parcel touches a 50-meter buffer around a point, it receives a "buffer" tag, which is only used to resolve land uses if a parcel’s original land use code is vacant or null.‌
The process of typing parcels using a polygon dataset is exemplified in Figure 4. The campus geometry defined by the Census TIGER dataset is shown with the orange boundary, while parcels typed as Campus College - Low are shaded blue. All parcels where more than 25% of the polygon intersect with the landmark geometry are retyped accordingly. The large parcel in the upper right side of the image is not typed as campus, as less than 25% of its area intersects with the landmark polygon.​‌
Figure 4: Polygon-based typing using the Census Landmarks Dataset
Figure 5 below shows examples of direct and buffered tagging using point data, in this case the SABINS schools dataset (which provides point locations of K-12 schools in the National Center for Education Statistics' Common Core of Data). The blue parcels are all typed correctly as schools, either through direct tagging, or buffer tagging and subsequent typing because the parcels had null land use codes.​‌
Figure 5. Point-based Typing of Schools using SABINS Dataset
Lastly, there are some supplemental sources that are used to assign land use types directly. For example, golf courses are assigned the Golf Course Building Type. The UrbanFootprint generalized land use designation plays a role only for disaggregating data as part of a later step (Disaggregating Block-Level Data to Parcels).‌

Table 1. Supplemental Datasets for Built Form Typing and Disaggregation

Geometry Type
UF Land Use/Built Form
SafeGraph Points of Interest
OpenStreetMap (OSM)
Both Point and Polygon
See Table 2.
Parks and recreation
Prison Facilities
Correctional Facilities
Places of Worship
Religious Centers
Major Sporting Venues
Commercial Recreation except for Golf Courses which have their own Built Form
Fire Stations
Primary and Secondary Education²
Air Transportation
Colleges and Universities
Higher Education

Table 2. OpenStreetMap (OSM) Data Crosswalk

OSM Property
OSM Tags
UF Land Use
Commercial All
Industrial All
All Forest
All Retail Services
Religious Centers
Primary/Secondary Education
All Retail Services
Strip Commercial Center
house residential
Residential All
All Multifamily
Single-Family Detached

Table 3. Census MAF/TIGER Feature Class Code Crosswalk

Building Type
MTFCC Description
Campus - College (High)
University or College,
Airport—Intermodal, Transportation, Hub/Terminal
Airport—Statistical Representation
Airport or Airfield
National Park Service Land
National Forest or Other Federal Land
Tribal Park, Forest, or Recreation Area
State Park, Forest, or Recreation Area
Regional Park, Forest, or Recreation Area
County Park, Forest, or Recreation Area
County Subdivision Park, Forest, or Recreation Area
Incorporated Place Park, Forest, or Recreation Area
Private Park, Forest, or Recreation Area
Other Park, Forest, or Recreation Area
Golf Course
Golf Course
Hospital/Hospice/Urgent Care Facility
Urban Civic
Government Center
Correctional Facility
Juvenile Institution

Dwelling Units

Where available, dwelling unit counts from Core Logic are used to populate the dwelling unit values in the Base Canvas. If not available, census data and land use information are used together to impute unit counts.‌
  1. 1.
    Assign dwelling unit counts from the raw CoreLogic data
  2. 2.
    Resolve condominium counts from CoreLogic
  3. 3.
    Impute missing data using land use information
  4. 4.
    Assign units from census data where necessary
As part of the process, the raw CoreLogic data is put through a series of standardization steps to address abnormalities and make it usable.‌

Dwelling Units from Parcel Data

CoreLogic provides two attribute columns that help identify the number of dwelling units present on a parcel: Building Units and Units Number. The definition of each is shown in Table 4.‌

Table 4. CoreLogic Dwelling Unit Attributes

CoreLogic Column
Column Description
Total Number of Buildings on the Parcel
Number of Residential, Apartment, or Business Units
‌The Building Units attribute is useful for identifying single-family homes. However, it is insufficient for calculating numbers of multifamily units as it would only count the building that houses all of the units. For that reason, we use the Units Number attribute to count the numbers of multifamily dwelling units.‌
As Units Number contains information on business units as well as residential units, the first step is to differentiate between the two. We do this by using the CoreLogic land use codes to classify parcels as residential, commercial/employment, or mixed use. Residential and mixed use parcels are assigned their Units Number value as the count of dwelling units, while parcels classified as commercial/employment are ignored.‌

Resolving Condominium Counts

The raw parcel data is inconsistent in its representation of unit counts in developments such as condominiums or master planned subdivisions. In some cases, the Units Number attribute refers to the number of units present in the entire development, rather than the units present on a single parcel polygon. For example, a master planned area outside of Phoenix might be subdivided into 164 plots, each housing a single family home. The raw data reports the Units Number as 164 for every polygon. If these counts were applied directly to each parcel, the number of units would be drastically overcounted. We resolve this by grouping all parcels that are a part of a development, then evenly distributing the dwelling units across residential parcels in the group.‌

Imputing Dwelling Unit Counts

There are many places where the CoreLogic data does not supply dwelling unit information. In these cases, we use a variety of methods to impute the dwelling unit counts.‌
Single family units
As a first step, we directly assign a single dwelling unit to all parcels coded with single-family land uses. The CoreLogic land use codes that fall in this category can be seen in Table 5 below.‌

Table 5. Single Family Residential Land Use Codes for Imputation

CoreLogic Land Use Code
Land Use Name
CoreLogic also uses two land use codes (see Table 6) that usually denote undeveloped parcels, and may include residential and/or non-residential uses. In these cases, we look to the Improvement Value attribute and assign a dwelling unit if the value surpasses a bare-minimum threshold of $5,000.‌

Table 6. Generic Residential Land Use Codes

CoreLogic Land Use Code
Land Use Name
Multifamily units
Multifamily unit counts for parcels missing dwelling unit data are assumed based on the CoreLogic land use codes shown in Table 7. For parcels coded with the "Multifamily Dwelling Unit" or "Apartment" land uses, a conservative density of 12 DU/acre is applied. For parcels coded with the "Multifamily 10 Units Plus" land use, a conservative estimate of 10 dwelling units is assigned.‌

Table 7. Multifamily Land Use Codes for Imputation

Land Use Name

Removing Outliers

The next step is to remove clear outliers, or cases where the resulting dwelling unit density is far beyond what could be considered reasonable. For parcels with CoreLogic dwelling unit counts and coded with single-family land uses (see Table 5), the following corrections are applied:‌
  • Parcels under 0.15 acre with more than five detached single-family dwelling units → reassigned one unit
  • Parcels with detached single-family unit density of 50 units/acre, well exceeding what is viable → reassigned units at a density of 5 units/acre
For parcels coded with multifamily land uses (see Table 7):‌
  • Parcels with a multifamily unit density over 1000 units/acre, well exceeding what is viable → reassigned units at a density of 10 units/acre

Dwelling Units from Census Data

Even after all of these transformations, there are still instances where the raw parcel data simply does not provide enough information to impute dwelling units. In these cases, data from the UrbanFootprint census block-level canvas is used to identify and fill in gaps. Parcel-level data is first aggregated to the block scale to allow for a direct comparison of dwelling unit totals against the block canvas totals, which come from the 2010 US Decennial Census as the latest release with block-level data. Blocks that satisfy the following conditions are flagged as cases where raw parcel data should be substituted with census dwelling unit totals:‌
  • Blocks that have at least 10 dwelling units in the 2010 Decennial Census
  • Blocks where the aggregate parcel dwelling unit total is more than 30% lower than the 2010 Decennial Census block total
For parcels that match these cases, block-level totals are disaggregated down to parcels using land use codes to identify parcels that can accommodate residential data (residential or mixed use parcels). This process is detailed further in Disaggregating Block-Level Data to Parcels.‌

Source-Specific Modifications

Beyond the process described above, some modifications need to be made in cases where unit counts or land use codes originating with the original parcel source data are not consistent with the CoreLogic attribute definitions, or where the values do not follow the pattern of the rest of the dataset. In these cases, modifications are made. There are several types of data inconsistencies: unreliable building units data; overtyping with the apartment land use code; overtyping with the single family residential land use code; and the representation of attached single-family units.
Counties where these modifications are applied can be found in Appendix B. Each modification is described in more detail below.‌

Unreliable Building Units Data

In some counties, the Building Units field accounts for structures such as small sheds or storage areas. In other cases, the raw building units data is unreliable when compared to satellite imagery, overcounting the actual number of buildings on detached single family parcels. For counties where these patterns are identified, the building units field is ignored and instead dwelling unit counts are typed solely using the land use code imputation process.‌

Overtyping with Apartment Land Use Code

In some cases, the CoreLogic "Apartment" land use code is used to denote rented units, rather than providing information as to whether the units are in multifamily structures or not. To improve the accuracy of the UrbanFootprint Building Type assigned to these parcels, they are given the detached single-family general land use designation.‌

Overtyping with Single-Family Land Use‌ Code

The "Single Family Residential (SFR)" land use code is most commonly applied to parcels where there is a dwelling unit on the structure, while vacant residential land uses are denoted with a Vacant land use (#465). That said, there are counties where the SFR type is liberally applied to any residential use. In these cases, the dwelling unit imputation process for SFR is skipped; the "Building Units" field to assign dwelling units instead.‌

Application of Attached SIngle-Family Types

Throughout most of the country, duplex and triplex units are parcelized in such a way that each unit has its own geometry. Where building unit data is missing, unit counts are imputed such that each geometry receives one dwelling unit. In other cases, the Duplex, Triplex, and Quadruplex land use designations are used to represent single parcels that contain more than one unit. For these places, where building unit data is missing, dwelling units are imputed using a literal application of the unit type (i.e., Duplexes receive two units, Triplexes receive three units, etc.).‌

Population and Households

Values for population and households are derived using the dwelling unit counts by type. The number of dwelling units present on each parcel is multiplied by census rates (ACS 2019 5-Year Estimates) for occupancy to estimate households (households are defined as occupied dwelling units). Population is then calculated using census-derived rates for household size by dwelling unit type (single family detached, single family attached, and multifamily) at the tract level.‌
When there are dwelling units in a tract but the tract has null or zero rates from the census, we use the calculated average of the rates of nearby tracts with a similar LSAD designation.‌

Employment by Category

Employment by category is first estimated at the census block level using job location data from the US Census Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) dataset (2018). The block-level employment counts are classified by North American Industry Classification System (NAICS) code, which are crosswalked to the employment subsectors used by UrbanFootprint (see Appendix C for the crosswalk table). The employment counts are then disaggregated down to parcels using the process described in the following section.‌

Disaggregating Block-Level Data to Parcels

Where data is not available at the parcel level, census block-level counts for dwelling units and employment is disaggregated down to the parcels. Disaggregation is guided by the parcels' general land use designation, each of which has rules as to the types of dwelling units and/or employment sectors it can include. For example, a parcel with a "Single Family Detached" land use designation can take on dwelling units, but not employment, from its parent block. Similarly, a parcel with a "Retail" land use designation can take on retail employees, but not industrial employees.‌
From there, dwelling units and employment are distributed among parcels in proportion to their land area, such that larger parcels receive more jobs while smaller parcels receive less.‌
By default, parcels that are classified as "Vacant," "Open Space," or "Other" are excluded from disaggregation. That said, there are some exceptions. If there are dwelling unit or employment counts at the block level, yet all parcels within the block are classified as one of "Vacant," "Open Space," or "Other," the dwelling units or employment get assigned to only the "Open Space" or "Other" parcels. If all the parcels are "Vacant," the disaggregation logic distributes the dwelling units and employment by land area.‌
Lastly, data is not disaggregated to parcels under 100 square feet.‌

Building Area by Type

The building area fields in the Base Canvas are populated using logic similar to that used for dwelling units. Where the CoreLogic parcel data contains information on building area, it is incorporated directly into the parcel canvas. If missing, building area is imputed based on default values for square feet per dwelling unit and per employee by subsector.‌
Building area in the Base Canvas is defined as the total living area, referring to area that would be heated or cooled. This typically excludes garages, unfinished basements, and patios. The CoreLogic Living Square Feet attribute is used to populate the building area columns. For reference, the other CoreLogic building area fields are summarized in Table 8.‌

Table 8. CoreLogic Building Area Attributes

CoreLogic Attribute Name
The Building Square Footage that can most accurately be used for assessments or comparables (e.g., Living, Adjusted, Gross).
The codes appearing in this field indicates the source used to populate the UNIVERSAL BUILDING SQUARE FEET field (e.g., Living, Adjusted, Gross). Please see "BLDSF" table for code descriptions.
The size of the building in Square Feet. This field is most commonly populated as a cumulative total when a county does not differentiate between Living and Non-living areas.
This is the area of a building that is used for general living. This is typically the area of a building that is heated or air conditioned and generally does not include Garage, Porch or Unfinished Basement Square Footage values.
Square footage of the part of the building which is level with the ground (typically the front of the building). This is generally above the basement(s) and below the second floor.
This is the square footage for the entire building. Typically this represents all square feet under the roof.
This is the square footage used by the county or local taxing / assessment authority to determine Improvement Value. This figure is typically 100% of the living area, plus lower percentage of non-living area.
This is total square footage associated with Basement portion of a building. This would include both finished and unfinished areas.
This is the total square footage of the primary garage or parking area (i.e., commercial). This includes both finished and unfinished areas.
The total living square feet for a parcel is allocated to the canvas attributes for building area by housing type and employment by subsector according to the dwelling units and employment present on the parcel. The logic used to distribute the square footage is summarized in Table 9.‌

Table 9. Building Area Distribution Logic

Assignment Logic
Dwelling Units > 0 AND
Employment = 0
Assign Living Square Feet data to housing type present on the parcel
Employment > 0 AND
Dwelling Units = 0
Proportionally distribute building area based on the number of employees in each subcategory
Dwelling Units > 0 AND
Employment > 0
Distribute Living Square Feet into residential and employment uses based on dwelling unit vs. employee proportions. Then assign using the methodology for each case described above.
For cases where Living Square Feet data is missing for the parcel, building area is imputed using default assumptions for building area per unit by housing type, and per employee by subsector. The assumptions vary according to broader land development category as identified by the intersection density of the census block in which a parcel is located. Areas with intersection densities above 150 per square mile are considered Urban or Compact, while those with lower densities can be Suburban or Rural. The assumptions are summarized in Table 9.‌

Table 9. Default Building Area Assumptions

Building Area Field
Square Feet per Dwelling Unit or Employee
Square Feet per Dwelling Unit or Employee
(Intersection Density >= 150 per square mile)
(Intersection Density < 150 per square mile)
Small Lot Detached- Single-Family
Large Lot
Detached- Single-Family
Attached- Single-Family
Multifamily (2– 4 units in structure)
Multifamily (5+ units in structure)
Retail Services
Other Services
Office Services
Public Admin
Medical Services

Parcel Area by Land Use

The Base Canvas includes parcel area attributes that can be used to track land area for residential, employment, and mixed use development. Parcel area values correspond to the total area of a parcel; that is, the land area is not divided up in any way to reflect different uses within a single parcel. Parcel area is first allocated to one of four mutually exclusive top-level categories according to the criteria outlined in Table 10.‌

Table 10. Top-Level Parcel Area Categories

Parcel Area Category
Parcels that have dwelling units and no employment
Parcels that have employment and no dwelling units
Mixed Use
Parcels that have both dwelling units and employment
No Use
Parcels that have neither dwelling units or employment
Within the top-level residential and employment categories, there are subcategories by dwelling type and employment sector. These parcel area subcategories are not mutually exclusive – each receives the total parcel area if the associated uses are present on the parcel. For example, if a parcel has both retail employment and office employment, both the retail parcel area and office parcel area will be populated with the same value – that for the total area of the parcel. Table 11 includes a full list of the parcel area columns.‌

Table 11. All Parcel Area Attributes

Parcel Area Column Name
Column Key
Residential Parcel Area
All Single Family Detached Parcel Area
Small Lot Detached Single Family Parcel Area
Large Lot Detached Single Family Parcel Area
Attached Single Family Parcel Area
Multifamily Parcel Area
Employment Parcel Area
All Retail Parcel Area
All Office Parcel Area
All Public Parcel Area
All Industrial Parcel Area
All Agriculture Parcel Area
All Military Parcel Area
Mixed Use Parcel Area
No Use Parcel Area

UrbanFootprint Land Use Typing

UrbanFootprint represents land use on parcels using Building Types. Building Types nest within a classification system composed of four levels, offering users the flexibility to depict development at various degrees of detail. The hierarchy of categories ranges from a high-level summary category (L1) down to specific Building Types and Place Types (L4) (see Land Use Hierarchy for more information). Each feature in the Base Canvas is categorized according to all levels.‌
As part of the parcel canvas creation process, each parcel is assigned a Building Type from UrbanFootprint's default library from among those prescribed for its general land use designation (as described earlier) using density, and, where applicable, land use information from supplemental datasets, to select the best fit. The values for the higher-level L1 to L3 categories are automatically generated via the Building Type designation.‌

Density-Based Classification

As described earlier, parcels are first assigned an UrbanFootprint general land use designation based on their CoreLogic land use codes (see Crosswalking to General Land Use Classifications). Each general land use designation is associated with one or more Building Types, effectively narrowing down the potential candidates. In this step, the Building Type that "most closely" matches the density of each parcel is identified.‌
“Closeness” is measured as the lowest standardized absolute difference between the Building Type and parcel densities. To do this, the densities of the parcels and Building Types are first standardized into scores by subtracting the mean and dividing by the standard deviation of the corresponding building types set. Then, each parcel’s standardized density score is compared to the standardized scores of its candidate Building Types. The differences are then squared and summed. The Building Type that corresponds to the least sum-of-squares is selected and assigned to the parcel.​‌
Could not load image
Least sum-of-squares equation
If the dwelling unit density and employment density were the axes of a two-dimensional graph, this sum-of-squares would represent the distance between the parcel’s data point and the Building Type data point. Therefore, the least sum-of-squares would represent the building type that is closest to the parcel’s data point. Other attributes could be represented similarly on an n-dimensional graph.‌
Currently, the process uses dwelling unit density for residential land use designations, employment density for employment land use designations, and both for mixed land use designations.‌

Non-Density Based Classification

Relying purely on the density-based approach will not capture Building Types representative of special land uses such as parks, open space, water, schools, or cemeteries. These types are not density-based, so their identification is based on the use of supplemental datasets (see the Supplemental Datasets section for more details). A few exceptions are detailed below.‌
Parcels are assigned the "Water" land use if census data indicates that the area of the parent block geography is covered entirely by water³, unless they are classified with residential land uses. This exception accounts for residential parcels at the edge of water bodies.‌
Institutional types, such as courthouses, libraries, or city halls, cannot be identified using the density-based approach. In the CoreLogic data, these parcels are sporadically categorized as "public," "tax exempt," or "state property," all of which are hard to parse into specific Building Types. If these parcels have not already been typed using supplemental datasets, they are categorized as "Open Space" if they are rural (often they are state or regional parks), "Non-Urban Civic" if they are in developed areas with intersection densities under 150 per square mile, or "Urban Civic" if they are in developed areas with intersection densities over 150 per square mile.‌

Intersection Density and Land Development Category

Intersection density and Land Development Category are attributes that reflect the land use context of a parcel. Both are set at the census block level, then passed down to parcels. Each parcel is assigned the intersection density and Land Development Category of the block with which it shares the most area.‌

Intersection Density

Intersection density is recognized as a proxy for walkability. An intersection is defined as the intersection of any two walk or drive network segments, as derived from OpenStreetMap (OSM) data. Intersections within 15 meters (about 50 feet) of each other are consolidated into a single intersection. This resolves counts for boulevard intersections and other cases where the network configuration can lead to overcounting (for example, by capturing two sides of the same intersection as separate intersections). Intersection densities are calculated over a buffered area of 400 meters around each block to smooth out local variations and normalize densities for all locations with respect to their surroundings.‌

Land Development Category

UrbanFootprint Land Development Category is a classification that reflects broad development patterns. They include Urban Infill (“Urban”), Compact Walkable (“Compact”), and Suburban (“Standard”). The Urban category represents areas (typically within moderate and high density urban centers) that have the highest intensity and mix of uses. Compact areas are less intensely developed than Urban areas but very walkable in part because of their mix of residential, commercial, and civic uses. “Standard” represents auto-oriented, separate-use suburban development patterns. (For custom canvases, a “Rural” category can be used to represent rural development.)‌
Land Development Category is assigned to census blocks, and in turn parcels, according to two criteria: intersection density per square mile and activity density (i.e., dwelling unit and employment densities). The categories are used in Base Canvas land use typing, as well some analysis modules (namely the Fiscal Impacts module). The categories also serve to communicate scenario concepts and results.‌
The criteria for the categories are summarized in Table 12.‌

Table 12. Land Development Category Criteria

Land Development Category
Intersection density >= 150 per square mile, and Employees/gross acre > 70 or dwelling units/gross acre > 40
Intersection density >= 150 per square mile, and Employees/gross acre <= 70 or dwelling units/gross acre > <=40
Standard (Suburban)
Intersection density < 150 per square mile
Guidelines based on local conditions

Irrigated Area

Lastly, the Base Canvas includes estimates of residential and commercial irrigated area. The values in the parcel canvas are modeled based on general assumptions for the percentage of parcel area that is irrigated. Assumptions are associated with the Building Type assigned to each parcel.‌


¹ The MAF/TIGER Feature Class Code (MTFCC) is a 5-digit code assigned by the Census Bureau intended to classify and describe geographic objects or features. These codes can be found in the TIGER/Line products.
² The schools are classified as elementary, middle or high based on the highest grade offered at the school. Schools are typed as ‘urban’ if the intersection density of the surrounding census block is greater than 150 intersections per square mile, and ‘non-urban’ if the intersection density falls below this threshold.
³ Census blocks where AWATER > 0 and ALAND = 0.

Release Notes

January 2022

  • Updated building footprints data. An update to the underlying building footprints data (Microsoft Building Footprints) has increased the coverage of our dwelling units data and irrigated land area calculations, and in some cases has improved our commercial land use typing.
  • Improved commercial land use typing. An update to the underlying point-of-interest data has improves the typing of commercial land uses.
  • More accurate land use and employment information. An update to the underlying OpenStreetMap point and polygon data yields more accurate land use typing and employment information.
  • Bug Fix: Previously, calculated columns in the parcel reference data layer such as Floor Area Ratio, Improvement to Land Value Ratio, Market Improvement to Land Value Ratio, and Assessed Improvement to Land Value Ratio erroneously reported a zero when inputs to the calculation were missing or not applicable. With this release, the calculated value will also be reported as not available in those cases.

September 2021

  • Addition of Oconee County, GA
  • Addition of download capabilities for Imperial County, CA and Cochise County, AZ
  • Updated parcel geometries and attribute data for hundreds of U.S. counties
  • Improved typing of commercial land use

July 2021

  • Expanded coverage. The Base Canvas for California has been expanded to cover an additional 15 counties for complete parcel coverage of the entire state. Parcel geometries and attribute coverage have also been updated for hundreds of counties across the U.S.
  • Improved commercial land use typing. An update to the underlying point-of-interest data improves the typing of commercial land uses.
  • More accurate land use and employment information. An update to the underlying OpenStreetMap point and polygon data yields more accurate land use typing and employment information.

April 2021

Updated point-of-interest data. Updated point-of-interest (POI) data from SafeGraph and OpenStreetMap, now current as of February 2021, improve the accuracy of commercial typing and employment information assigned to parcels.
  • Updated American Community Survey (ACS) data. The Base Canvas now uses ACS 2019 5-year estimates of population and households.
  • Updated parcel geometries and attributes. Parcel geometries have been updated for 183 counties. Parcel attributes have been updated for 1,023 counties.

January 2021

Updated employment data. By updating the underlying data source from the U.S. Census Bureau’s LODES 2017 to the newly released LODES 2018, the UrbanFootprint Base Canvas now provides you with the latest available census-based employment data, adding nearly 3.5 million jobs nationwide.
  • Updated parcel geometries and attributes. Parcel geometries have been updated for 132 counties. Parcel attributes have been updated for 1,022 counties.
  • More accurate typing of parks and multifamily parcels. The assignment of current land use to parcels with parks and multi-family residential buildings has been improved to result in fewer incorrectly assigned park or residential parcels.

October 2020

In addition to our regular update, this quarter’s release includes some exciting improvements:‌
  • More accurate typing of commercial parcels based on newly integrated SafeGraph point-of-interest (POI) data, improving how commercial use is identified (including offices, retail sites, medical services) and how job categories are separated at the parcel level
  • Updated census metrics, using the latest Census ACS 2018 dataset, to improve how missing population or household rates are handled at the census tract level

May 2020

Source data

This update incorporates the following source data:‌
  • Parcel information. CoreLogic parcel data (Q1 2020)
  • Locations of interest. OpenStreetMaps (OSM) locations of interest (point and polygon) data (Feb 2020)
  • Employment. Census Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data (Latest release, 2017)

Logic updates

Vacant parcels have sometimes been typed incorrectly as commercial property. We’ve updated our logic to use building footprints to better understand where properties are unlikely to be used currently for commercial purposes. This change, along with some improvements to how we're using OSM data for locations of interest, means that you'll likely see some of the following differences:‌
  • Better identification of parks, golf courses, and open spaces
  • More "blank" parcels, fewer inaccurate commercial parcels. "Blank" parcels occur when we don’t have data to reliably identify the current use for the parcel. In some cases, these parcels may have been subdivided in anticipation of future development. In other cases, we simply have conflicting information about the current land use. The OSM data helps us better identify uses.


We're always looking to improve the accuracy of our Base Canvas. If you would like to help us make improvements for yourself and other users, please use this form to report incorrectly typed parcels: Your input is appreciated!‌

Appendix A: Base Canvas Attributes

The following table provides a reference to the names, column keys (which are used in exports of the data table), and descriptions of the Base Canvas attributes.
Attribute Name
Column Key
Geography ID
A unique identifier for the geographic feature.
Land Use Summary (L1)
The highest-level land use category, which includes broad classifications such as Residential, Commercial, and Mixed Use. All lower-level categories nest within higher-level categories.
Land Use Summary (L2)
Second-level land use category, which includes summary classifications such as Single-family and Multifamily.
Land Use Category (L3)
Most detailed land use category, which corresponds to specific land uses such as Single-family detached.
Land Use Type (L4) (or Built Form Type)
Building Type or Place Type of the canvas geometry.
Land Development Category
A broad categorization of land use patterns based on intersection density, housing density, and employment density. The categories include Urban, Compact, Standard (suburban), and Rural.
Intersection Density
Density of roadway intersections per square mile, measured over a buffered area of the canvas geometry.
Gross Area
Gross area of the canvas geometry.
Residential population associated with occupied dwelling units. This excludes people residing in group quarters.
Households, equivalent to occupied dwelling units.
Dwelling Units
Dwelling units, including occupied and unoccupied units.
All Detached Single-Family Dwelling Units
Total detached single family homes.
Large Lot Detached Single-Family Dwelling Units
Detached single family homes on lots larger than 5,500 square feet.
Small Lot Detached Single-Family Dwelling Units
Detached single family homes on lots smaller than 5,500 square feet.
Attached Single-Family Dwelling Units (Townhomes)
Attached single family homes, including townhomes, rowhouses, and other units that share walls but are not stacked vertically.
All Multifamily Dwelling Units
Homes in buildings that contain at least two housing units that are adjacent vertically, or horizontally with shared utility systems.
Multi-Family - 2 to 4 Dwelling Units
Homes in buildings that contain two to four housing units that are adjacent vertically, or horizontally with shared utility systems.
Multi-Family - 5 or More Dwelling Units
Homes in buildings that contain more than five housing units that are adjacent vertically, or horizontally with shared utility systems.
Total jobs across all employment categories.

Appendix B: Source-Specific Modifications by County

Unreliable Building Units
Overtyped Apartments
Overtyped Single Family
Attached Single Family Applied
Morgan County
Kenai Peninsula
Matanuska-Susitna Borough
Madera County
Nevada County
Charlotte County
Walton County
Bartow County
Floyd County
Livingston County
Olmsted County
Otter Tail County
Sherburne County
Winona County
Wright County
Pearl River County
New Hampshire
Belknap County
New York
Columbia County
Dutchess County

​Appendix C: NAICS Code Crosswalk to UrbanFootprint Employment Categories

The Census Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) Workplace Area Characteristics (WAC) dataset accounts for employment using North American Industry Classification System (NAICS) codes, which are mapped to the employment subsectors used in UrbanFootprint as follows.
UrbanFootprint Canvas Employment Category
LODES WAC Field Code
NAICS Sector
Extraction Employment
Number of jobs in NAICS sector 21 (Mining, Quarrying, and Oil and Gas Extraction)
Agriculture Employment
Number of jobs in NAICS sector 11 (Agriculture, Forestry, Fishing and Hunting)
Utilities Employment
Number of jobs in NAICS sector 22 (Utilities)
Construction Employment
Number of jobs in NAICS sector 23 (Construction)
Manufacturing Employment
Number of jobs in NAICS sector 31-33 (Manufacturing)
Wholesale Employment
Number of jobs in NAICS sector 42 (Wholesale Trade)
Retail Services Employment
Number of jobs in NAICS sector 44-45 (Retail Trade)
Transport Warehousing Employment
Number of jobs in NAICS sector 48-49 (Transportation and Warehousing)
Office Services Employment¹ (emp_office_services)
Number of jobs in NAICS sector 51 (Information)
Number of jobs in NAICS sector 52 (Finance and Insurance)
Number of jobs in NAICS sector 53 (Real Estate and Rental and Leasing)
Number of jobs in NAICS sector 54 (Professional, Scientific, and Technical Services)
Number of jobs in NAICS sector 55 (Management of Companies and Enterprises)
Number of jobs in NAICS sector 56 (Administrative and Support and Waste Management and Remediation Services)
Education Employment (emp_education)
Number of jobs in NAICS sector 61 (Educational Services)
Medical Services Employment
Number of jobs in NAICS sector 62 (Health Care and Social Assistance)
Arts & Entertainment Employment
Number of jobs in NAICS sector 71 (Arts, Entertainment, and Recreation)
Restaurant Employment²
Number of jobs in NAICS sector 721 (Accommodation)
Accommodation Employment³
Number of jobs in NAICS sector 722 (Food Services)
¹ emp_office_services is the sum of employment counts for NAICS sectors 51 - 56.
² Restaurant and Accommodation employees are grouped into the same two-digit NAICS sector. Accommodation and Food Services employees are assumed to be equally split between restaurant and accommodation sectors for the purpose of separating these sectors for the default block-level canvas.
³ Restaurant and Accommodation employees are grouped into the same two-digit NAICS sector. Accommodation and Food Services employees are assumed to be equally split between restaurant and accommodation sectors for the purpose of separating these sectors for the default block-level canvas.
⁴ Four-digit NAICS codes are not available in the census LODES dataset, making it difficult to differentiate military employees from general public administration employees. emp_military therefore defaults to 0 in the block-level canvas. When better NAICS employment data or land use codes are available, military employees should be separated from emp_public_admin.