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Global Building Morphology Indicators (GBMI)

Synopsis

GBMI is an open project on systematising, computing, and storing individual and aggregated building form metrics, which may be useful for researchers and practitioners across multiple domains, e.g. urban data science.

This repository contains the list of hundreds of indicators from building footprints, which we have identified in our research, and the code to implement them in a database. For an overview of the entire research (method, systematic literature review, data, code, examples of analyses...), please visit the GBMI project website or the research paper published in CEUS. For the repository (i.e. ready-to-use pre-computed datasets) covering dozens of selected urban areas around the world, please visit the website.

The code is a collection of bash scripts and SQL scripts that are run to perform the database setup, data ingestion, data transformation and analysis that yields the GBMI output, and the final data export. It takes OpenStreetMap (OSM) as input, but it may be adapted for other sources of data. The aggregation of the indicators works both for administrative areas and according to a regular grid (raster).

The scope of GBMI datasets could be global, extracting building data from the planet OSM data; or it could be focused on a selection of geographical regions such of countries, states, or cities. Each defined scope will be contained in its own database. The raster system (grid) is adopted from WorldPop, but it is also something that can be defined and swapped as it fits the research purpose.

The GBMI implementation requires a PostgreSQL 12 (or newer) database running on cloud services such as Amazon Web Services (AWS) RDS or a self-managed server. PostGIS and a few other extensions are also necessary for the spatial analysis. These extensions are: postgis, hstore, fuzzystrmatch, postgis_tiger_geocoder, postgis_topology.

Research paper and attribution

An open access paper describing the project, and from which the description above was adopted, was published in Computers, Environment and Urban Systems. Please refer to the paper for detailed information, while this website summarises the project and provides the links to the datasets and code.

If you use GBMI in a scientific context, please cite the paper:

Biljecki F, Chow YS (2022): Global Building Morphology Indicators. Computers, Environment and Urban Systems 95: 101809. doi: 10.1016/j.compenvurbsys.2022.101809

@article{2022_ceus_gbmi,
  author = {Biljecki, Filip and Chow, Yoong Shin},
  doi = {10.1016/j.compenvurbsys.2022.101809},
  journal = {Computers, Environment and Urban Systems},
  pages = {101809},
  title = {Global Building Morphology Indicators},
  volume = {95},
  year = {2022}
}

License

All aspects of the project are licenced according to CC BY 4.0. That means that you can use our work for pretty much anything as long as you attribute it (i.e. cite our paper above). The paper has been released under the same licence and it is open access.

About this Python Package

The GBMI dataset generation process generate more than 50 tables per country per raster system. The number of tables increases when more than one raster system is implemented. Keeping analysis pipeline consistent across selected geographical scopes, many parts of the scripts are repetitive. For ease of maintenance, this repository offers a framework that generates these bash scripts and SQL queries using templates.

The Python package is developed using Jinja2 templating engine to produce bash scripts and sql queries using predefined templates. The scripts are then executed for each of the pre-defined geographical scope of studies. The package aim to achieve the following goals:

  • enforce re-usable script as much as possible
  • maintain consistency of analysis across all databases (each of which represents geographical scope)
  • manage/apply/adopt changes with ease across all databases

The package consists of these modules:

  • configurations.py: this modules reads and validates the config.json file
  • core.py: this module consists of the QueryParamExpander and QueryGenerator classes. The QueryParamExpander expands parameters defined in config.json for all combination of database, rasters and aggregation levels; whereas the QueryGenerator calls the Jinja2 template engine API to generate all the bash scripts and query scripts
  • logging.py: this module consists of a logging class that logs and feedback on the running/output status of the python package in generating those scripts

We run the package by python main.py. The generation of all queries and scripts takes more than 5 mins. To speed up the process or updates and/or bug fixes in specific sections, the python main.py command also takes one or more section key arguments (namely a-db-setup b-osm-rasters-gadm c0-misc c1-gbmi and/or d-export). This way, we could selectively generate scripts and queries for one or more specific sections.

Configuring the config.json

The python package relies on the configurations in the config.json to expand and generate bash scripts and sql query scripts for respective database, targeting corresponding raster systems and aggregated levels.

The following are the highlights of key configurations:

template_dirname: the directory name where template is store, relative to root of the package

output_dirname: the directory name where scripts are output, relative to root of the package

parameters > common: these are the commonly shared database/server instance related properties:

  • host_address: database host address
  • db_script_dir: directory path where db setup queries are stored
  • public_schema: public schema name
  • public_script_dir: directory path where queries for generating tables in public queries are stored (generally GADM and raster related)
  • misc_schema: schema name for miscellaneous tables
  • misc_script_dir: directory path where queries for generating tables in miscellaneous schema are stored
  • gbmi_schema: schema name for gmbi tables
  • gbmi_script_dir: directory path where queries for generating tables in gbmi schema are stored
  • qa_schema: schema name for quality analysis related tables
  • qa_script_dir: directory path where queries for generating tables in quality analysis schema
  • base_source_dir: base directory path where base source such as GADM and country codes are stored
  • site_source_dir: base directory path where gbmi source data are stored. Currently, each geographical scope has its own sub-directory that contains the source OSM and rasters
  • country_codes_dir: directory name where country codes csv is stored
  • country_codes_file: file name of the country code files
  • gadm_source_dir: directory name where gadm source files are stored
  • gadm_source_file: source file name of the gadm shapefile
  • gadm_target_table: target table when loading gadm shapefile
  • export_base_dir: base directory path when exporting gbmi databases/tables
  • export_script_dir: directory path where export query templates are stored

parameters > a-db-setup: these are parameters that are expanded to generate database set up scripts for each database respectively

  • databases: an array of database to be created/setup
  • users: an array of users/roles to be created, who are granted connect and read permissions to databases, schemas, tables and views
  • superusers: an array of superusers to be created, who will be granted superuser privileges to create/drop databases, schemas, tables and views.

parameters > b-osm-rasters-gadm: these are parameters that are expanded for each database to generate scripts that load osm, gadm and rasters, and queries that generate subsequent related tables

  • osm_source_files: an array of osm source files and its corresponding database
  • raster_names: an array of raster names and corresponding file suffix. The raster_population is optional, as it is only available with World Population rasters.
  • agg_levels: an array of aggregation levels for merging the GADM areas and geoms

parameters > c0-misc: these are parameters that are expanded to generate queries in miscellaneous schema, which is a pre-requisite before running the GBMI scripts

  • databases: an array of databases
  • raster_names: an array of raster names
  • agg_levels: an array of aggregation levels applied for analyzing building height and levels

parameters > c1-gbmi: there are the parameters that are expanded to generate bash scripts and queries for the GBMI pipeline

  • databases: an array of databases
  • raster_names: an array of rasters with optional raster_population and limit_buffer where applicable. For our research we apply the buffer limit of 50 to the global study.
  • buffers: an array of buffers for neighbour computations
  • agg_levels: an array of aggregation levels

parameters > d-export: these are the parameters expanded to generate bash scripts and query templates for GBMI data export

  • databases: an array of databases
  • raster_names: an array of rasters with optional raster_population and limit_buffer where applicable.
  • agg_levels: an array of aggregation levels

GBMI Process

PostgreSQL configurations

Before starting the GBMI process, please configure postgresql credentials in .pgpass and/or pg_hba.conf so the scripts can be run without being prompted for password.

The scripts are organized according to the following sections:

  • Database setup (a-db-setup)
  • OSM loading, global administrative boundaries and raster setup (b-osm-raster-gadm)
  • Misc scripts that analyze the building height and levels validity, and building tag value frequencies (c0-misc)
  • GBMI tables (c1-gbmi)
  • GBMI export templates and scripts (d-export)

Section a: PostGIS and OpenStreetMap Database Setup

The first step of the GBMI process is to create neccesary databases for each geographical scope, set up schemas, users and user privileges. The bash scripts and queries under this sections are generated based on the configurations in the config.json.

The bash scripts and queries under this section are to perform the following tasks:

  • create a database to host the openstreetmap data and install PostGIS and other necessary extensions
  • install these extensions: postgis, hstore, fuzzystrmatch, postgis_tiger_geocoder, postgis_topology
  • set up necessary schemas as configured in the configuration
  • create new users, if specified in the configuration, that set up privileges accordingly

Section b: Openstreetmap, Global Administrative Boundaries and Rasters

In this section, the scripts loads the Openstreetmap data, the GADM shapefiles and selected raster systems from the specified source directory in config.json.

OpenStreetMap

The OSM data are downloaded from Geofabrik server. If the scope/area of interest are not directly available, the larger area of the corresponding city/region is downloaded and further extracted using the osmium-tool program.

The extracted OSM is then loaded to PostGIS via osm2pgsql command.

GADM

To map the rasters and osm data to country, province/state, city/town etc more comprehensively, we use the Global Database of Global Administrative Areas. The GADM data is available in GeoPKG and Shapefile format. The GADM data can be downloaded from here.

The shapefile is then loaded to the PostGIS database via shp2pgsql command.

After the GADM shapefile is loaded, we subsequently compute the agggreated area and geometry of the administrative boundaries at admin level zero (country) through 5.

Rasters

In this research, we used the World Population 2020 rasters at two different resolutions: the 100m and the (aggregated globally) 1km resolutions. The geotiff files for worldpop could be downloaded from here and here.

Since the worldpop rasters are only available by country, these geotiff files are usually further extracted using QGIS to extract only the areas of interest.

After that, these rasters are loaded to the corresponding PostGIS database, based on the configurations in config.json, via raster2pgsql command.

The loaded rasters are then vectorized and mapped against the previously loaded GADM to obtain the country codes, admin division names.

Section c0: Miscellaneous

There are also a few tables that are helpful to inspect the quality and state of the OSM building data before proceeding to the GBMI data. These tables are:

  • osm_polygon_attr_freqs: this shows the frequency of values of building (osm_polygon) related tags. This is a pre-requisite table before started the GBMI pipeline.
  • agg_buildings_height_levels_qa_by_agg_level_raster_name: this shows aggregated statistics of completeness of building height and building levels

Section c1: Global Building Morphology Indicators

This section is the bash scripts and queries that create the building morphology indicator tables. The workflow and steps of computing the indicators are as follows:

Building Level Indicators:

  • buildings: extract mass majority of the buildings from OSM polygon table based on 12 most frequent 'building' tag value
  • building_by_raster: map buildings to each rasters we use in the system so we identify the country, province, state and city of the buildings
  • bga_by_raster: extract the geometric attributes that are needed for calculating the geometric building indicators from building_by_raster tables
  • bgi_by_raster: calculate the geometric building indicators from buidling_geom_attributes_by_rasster tables
  • bn_by_raster: extract neighbours for each of the buildings within the maximum buffer of our study defined in config.json. By definition of neighbours, the neighbour centroid has to fall within the ring buffer computed from the centroid of each respective buildings. Then ST_distance is used to compute distance between polygon to polygon.
  • bn_by_raster_centroid: extract neighbours for each of the buildings within the maximum buffer of our study defined in config.json. By definition of neighbours, the neighbour centroid has to fall within the ring buffer computed from the centroid of each respective buildings. Then ST_distance is used to compute distance between centroid to centroid.
  • bn_buffer_by_raster: calculate the neighbour indicators for each building within 3 different buffers: 25, 50 and 100, using bn_by_raster table.
  • bn_buffer_by_raster_centroid: calculate the neighbour indicators for each building within 3 different buffers: 25, 50 and 100, using bn_by_raster_centroid table.
  • bni_by_raster: joint table of the bn_buffer_by_raster at 3 different buffers
  • bni_by_raster_centroid: joint table of the bn_buffer_by_raster_centroid at 3 different buffers
  • buildings_indicators_by_raster: joint table of bgi_by_raster and bni_by_raster
  • buildings_indicators_by_raster_centroid: joint table of bgi_by_raster and bni_by_raster_centroid.

Aggregated Building Indicators:

The building indicators are aggregated at 5 GADM levels and at raster cell levels. Thus the aggregation levels are namely:

  • raster cell
  • country
  • province or state (admin division 1)
  • county or district (admin division 2)
  • town or city (admin division 3)
  • urban commune or municipals (admin division 4)
  • admin division 5

Three types of aggregated tables will be generated:

  • agg_bgi_by_agg_level_by_raster: this is aggregation of the geometric building indicators from bgi_by_raster tables
  • agg_bni_agg_level_by_raster: these tables combined the aggregation of the geometric building indicators and building neighbour indicators from bni_by_raster_name tables
  • agg_bni_agg_level_by_raster_centroid: these tables combined the aggregation of the geometric building indicators and building neighbour indicators from bni_by_raster_name_centroid tables

Section d: Exporting the Global Building Morphology Indicators

With the configuration in config.json of the target scope/datasets to be exported, the python script also generates a list of bash scripts, one for each dataset, to export the GBMI indicators. The current export process supports ESRI Shapefiles shp, GeoPackage gpkg, Comma Separated Values csv.

The exported files are organized under the following directory hierarchic structure:

  • {city}
    • {aggregation-level}
      • agg_bgi_by_{agg_level}_{raster_name}
      • agg_bni_by_{agg_level}_{raster_name}
      • agg_bni_by_{agg_level}_{raster_name}_centroid
    • bldg
      • buildings_indicators_{raster_name}
      • buildings_indicators_{raster_name}_centroid

The export of various format are stored under the {aggregation-level} folder.

Computed Global Building Morphological Indicators

The computed GBMI is a dataset with more than 380 fields. The export functions also mandate abbreviated fieldnames. Thus, the following is the data dictionary for all the exported fields.

GBMI Data Dict GBMI Data Dict GBMI Data Dict GBMI Data Dict GBMI Data Dict GBMI Data Dict GBMI Data Dict GBMI Data Dict

Contact

Yoong Shin Chow, Urban Analytics Lab, National University of Singapore, Singapore

Acknowledgements

GBMI is made possible by the efforts of many others, primarily developers of PostgreSQL/PostGIS and the OpenStreetMap community.

This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start Up Grant R-295-000-171-133 and by the AWS Cloud Credits for Research.

For more information, please see the aforementioned paper.

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