1.2. Asset Description

This study used a parcel-level inventory of buildings in the Bay Area that was developed by UrbanSim ([Waddell02]) using public resources such as the City and County of San Francisco’s data portal ([DataSF20]) and tax assessor databases.

The raw database, which includes 1.8 M San Francisco buildings, was shared in collaboration with UrbanSim. The UrbanSim database includes two files:

  1. Buildings File: a CSV file that contains building properties including total floor area, number of stories, year built, building occupancy, and parcel ID.

  2. Parcels File: a CSV file including the latitude and longitude of each building defined by the parcel ID.

A parsing application was built to collect the mentioned building properties from the UrbanSim building files. The occupancy ID (in integer) was used to infer the occupancy type and replacement cost per unit area (Table 1.2.1). For buildings with missing or invalid occupancy ID, the building was mapped to the default occupancy type (i.e., residential) with the average area of buildings in the inventory.

Table 1.2.1 Mapping rules for building occupancy type and replacement cost.

Occupancy ID

Occupancy Class

Replacement Cost / SQFT

Contents Factor

1/2/3/12

Residential

137.5

0.5

4/14

Office

131.9

1.0

5

Hotel

137.3

0.5

6

School

142.1

1.3

7

Industrial

97.5

1.5

8

Industrial

86.0

1.5

9

Industrial

104.0

1.5

10/11/13

Retail

105.3

1.0

Default

Residential

137.5

0.5

The structure types were also mapped from the building occupancy types along with the year built and number of stories. Unless the building was mapped to a single structure type, the structure type is considered random, in which case the mapped structure types are equally likely. Table 1.2.2 summarizes the structure type mapping.

Table 1.2.2 Mapping rules for structure type.

Year Built

Number of Stories

Occupancy

Mapped Structure Types

Older than 1900

Any

Any

Wood (W1) and Masonry (RM1, RM2, URM)

Newer than 1900

1~3

Residential

Wood (W1)

Newer than 1900

1~3

Comercial

Wood, Steel, Concrete, Reinforced Masonary (W1, S1, S2, C1, C2, C3, RM1, RM2)

Newer than 1900

1~3

Industrial

Steel, Concrete, and Reinforced Masonary (S1, S2, C1, C2, C3, RM1, RM2)

Newer than 1900

1~3

Other

Wood (W1)

Newer than 1900

4~7

Residential and Commercial

Wood, Steel, Concrete (W1, S1, S2, C1, C2)

Newer than 1900

4~7

Industrial

Steel, Concrete (S1, S2, C1, C2)

Newer than 1900

4~7

Other

Wood, Steel, Concrete (W1, S1, S2, C1, C2)

Newer than 1900

More than 7

Any

Steel and Concrete (S1, S2, C1, C2)

The available information about location and building geometry was further refined by merging the UrbanSim database with the publicly available Microsoft Building Footprint data ([Microsoft20]) for the testbed area. These data were used to populate two additional attributes, replacement cost and structure type, based on a ruleset that considers local design practice and real estate pricing. For further details about the database and ruleset, see [Elhadded19].

Waddell02

Waddell, P. (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning. J. Am. Planning Assoc. 68, 297–314. doi: 10.1080/01944360208976274

DataSF20

DataSF (2020). Building and Infrastructure Databases. San Francisco, SF: DataSF.

Microsoft20

Microsoft (2020). Microsoft Building Footprint Database for the United States. Washington, DC. https://www.microsoft.com/en-us/maps/building-footprints.

Elhadded19

Elhaddad, W., McKenna, F., Rynge, M., Lowe, J. B., Wang, C., and Zsarnoczay, A. (2019). NHERI-SimCenter/WorkflowRegionalEarthquake: rWHALE (Version v1.1.0). http://doi.org/10.5281/zenodo.2554610