Building data by state and county.
>>> from buildings import Buildings
>>> data = Buildings(state="NH",county="Grafton",aws_profile="gridlabd")
>>> data
climate year centroid footprint height ground_area code class mixed type windows floors floor_area
id
87MCM9QG+43HG-13-13-13-12 6A 1972 drv0d1h7y drv0d1hebqt,7t62,7gy3,kqfz 4.0 112.5 1 1 False 1 0.14 1 112.5
87MCM9QG+2325-11-11-13-11 6A 1972 drv0d1h6h drv0d1h66se,yu,7dx,krm,m9f,m2k,nhf,3u2v,45e,744 3.9 96.3 1 1 False 1 0.14 1 96.3
87MCM9QG+352F-11-9-10-8 6A 1972 drv0d1hdc drv0d1hddhr,e048,e0zx,g1q 3.0 49.5 1 1 False 1 0.14 1 49.5
87MCPFQ9+7RCP-14-12-14-13 6A 0 drv0u6316 drv0u63158x,28m,f55,t5k 28.2 136.3 5 1 False 0 0.14 1 136.3
87MCMCR3+56M2-10-4-10-4 6A 1988 drv0dc8em drv0dc8ej17,t52,w7z,n9d 4.6 53.7 2 1 False 5 0.14 1 53.7
... ... ... ... ... ... ... ... ... ... ... ... ... ...
87MCH6HR+6VM4-29-21-29-21 6A 1975 drszzj5s9 drszzj5mjz2,td9r,sjm8,eb7e 4.2 483.6 1 4 False 150 0.07 1 483.6
87MCPCVC+VP4X-17-17-19-17 6A 1972 drv0g5qr5 drv0g5qr2by,t3m,qzpd,qvdg,qty7,q6xy 4.1 242.4 1 8 False 5 0.21 1 242.4
87PC636M+9FXQ-16-14-17-13 6A 1920 druge825t druge825jyw,7wb,hjve,7bm3 7.4 148.5 1 1 False 20 0.14 2 296.9
87PC4397+H7H3-23-12-23-13 6A 0 drufgp95r drufgp968z0,4v6t,hn9y,7fn2 17.1 267.1 1 8 False 0 0.21 1 267.1
87M9PP46+R5Q3-16-16-16-16 6A 0 dru8eh8kh dru8eh87ggb,96,fkr,fwt,k162,kdsx,kect,ks5g,kqn... 7.0 207.0 2 1 False 21 0.14 2 414.1
[47529 rows x 13 columns]
Notes
- Prior to August 7, 2023 access is retricted to users with permissiong to read the AWS repository using an AWS profile for the HiPAS GridLAB-D project.
Field | Type | Description |
---|---|---|
id |
str |
Building unique identifier (see AutoBEM-4 for details) |
climate |
str |
Building climate zone |
year |
int |
Year of construction |
centroid |
str |
Geohash of building centroid |
footprint |
str |
Geohash of building footprint (see Note 1) |
height |
real |
Building height from imagery (m^2) |
ground_area |
real |
Building footprint area (m^2) |
code |
int |
Building code (see building_code.json ) |
class |
int |
Building class (see building_class.json ) |
mixed |
bool |
Mixed type flag |
type |
int |
Building type (see building_type.json ) |
windows |
real |
Average window wall ratio (pu wall area) |
floors |
int |
Number of floors |
floor_area |
real |
Total conditioned floor area (m^2) |
Notes:
- The building footprint is coded using geohashes, except that only the trailing characters of the hash code are recorded. For example
drv0d1hebqt,7t62,7gy3,kqfz
should be interpreted as
drv0d1hebqt,drv0d1h7t62,drv0d1h7gy3,drv0d1hkqfz
Some fields are encoded for storage efficient. Data accessors are provided to decode these fields.
Get the building centroid. Centroid are recorded with an accuracy of 2.4 cos(latitude) meters.
Get the building footprint. Footprint vertices are recorded with an accurage of 7.4 cos(latitude) meter.
Get the building class. See building_class.json
for a list of known classes.
Get the building construction code. See building_code.json
for a list of known building codes.
Get the building type. See building_type.json
for a list of known building types.
- New, Joshua et al, "AutoBEM-4", Oak Ridge National Laboratory, May 2023.