Sustainable Cities: Socioeconomics, Building Types, and Urban Morphology

Melissa Allen-Dumas, Christa Brelsford, Joshua New, Levi Sweet-Breu, Frank Li
Oak Ridge National Laboratory

Andy Berres
National Renewable Energy Laboratory

In urban environments, demographic, and infrastructural characteristics co-evolve and together determine risks, vulnerability and resilience. Infrastructure systems such as energy and water determine many environmental risks and provide access to various essential services. These risks and benefits are transferred across long distances and differentially across demographic and socioeconomic subgroups. Additionally, urban environments have significant effects on public health and population level resilience, especially to extreme events such as heat waves. However, interactions among urban microclimate, urban morphology, socioeconomic heterogeneity, and anthropogenic activities are not well understood. To begin to understand these interactions, our team has developed new datasets for the Los Angeles Metropolitan Statistical Area, and we challenge the participants to combine these data sets (and other relevant data of participants’ choosing) to answer our challenge questions.

We look forward to presentations using novel methods for interpreting and visualizing these data that draw on machine learning and other big data techniques, and we welcome new collaborations to complement the work of understanding how current and future neighborhood morphological patterns can contribute to the development of smart and sustainable cities.

• Los Angeles building shapefile dataset. Data
• Building archetypes for the city based on an open-source aggregation scheme that reflects the statistical occurrence of each building type in the city including estimates of building use type, number of stories or building height, total floor area, and year built for each building/parcel (see reference below).
  • Data type/size: Region (1.7kB), Models (1.9MB), Multipliers (11.3kB).
  • Data available from: https://zenodo.org/record/4726136#.Yl7YK8jMKUl
  • Example visualizations for areas other than LA can be found here https://bit.ly/virtual_vegas (just building geometry) or https://bit.ly/virtual_epb (analysis details)
  • More information available: https://bit.ly/AutoBEM
• Land Use, data DOI TBA
• Los Angeles Urban Parameters: square tiles covering Los Angeles containing 100m resolution urban parameters for each 5m vertical level. Parameters include frontal area density, plan area density, roof area density, mean building height, building surface to plan area ratio, complete aspect ratio, height to width ratio, and many more. Data DOI: https://doi.ccs.ornl.gov/ui/doi/373.
• Instructions for data download: https://smc-datachallenge.ornl.gov/wp-content/uploads/2022/05/Constellation_Downloading_Guide_v02.pdf

Challenge questions:

1. How does the number and arrangement of buildings, along with the building surface to plan area ratio in a given Los Angeles tile relate to building construction quality and value in the same geographic area?
2. What is the distribution of commercial, industrial, and residential buildings within each geographic location? Do these distributions correlate with building age? Building value? Building size?
3. Using temperature data from a source of the participant’s choosing, are there locations within the city that tend to be warmer than others? How does this relate to building density and building type?
4. Using demographic data from a source of the participant’s choosing, how does the built environment and the local scale experience of heat co-vary with socio-economic and demographic characteristics of residents?
5. Using the included land use data, how does greenspace vary with urban temperature and demographic distribution?

Reference:

1. New, Joshua R., Bass, Brett, Adams, Mark, Berres, Anne, and Luo, Xuan (2021). “Los Angeles County Archetypes in Weather Research and Forecasting (WRF) Region from ORNL’s AutoBEM [Data set].” Zenodo, doi.org/10.5281/zenodo.4726136, Apr. 28, 2021. [Data]