Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States.

Johannes H. Uhl,Stefan Leyk,Dylan S. Connor,Anna E. Braswell,Deborah Balk,Caitlin M. McShane

doi:10.5194/essd-13-119-2021

Abstract

The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).

Highlights

Over the last 200 years, the number of people living in urban areas in the United States has grown more than 800-fold, from around 320 000 and 6 % of the population in 1800 to 270 million and 80 % of the population by 2016 (US Census Bureau, 1993, 2016)
While advanced GIS practitioners would be able to derive the Built-up area (BUA) surfaces from the built-up property record (BUPR)–built-up property location (BUPL) datasets, we provide them as a separate dataset, to facilitate the use for applications where binary built-up–not built-up differentiation is sufficient
In order to examine if the ZTRAX data and the derived HISDAC-US data products exhibit this trend, we examined uncertainty trajectories across the rural–urban continuum, as modelled by the US Department of Agriculture (USDA) rural–urban continuum codes (RUCCs; for 2013; Butler, 1990)

Summary

Introduction

Over the last 200 years, the number of people living in urban areas in the United States has grown more than 800-fold, from around 320 000 and 6 % of the population in 1800 to 270 million and 80 % of the population by 2016 (US Census Bureau, 1993, 2016). The detailed spatial and temporal information provided in ZTRAX allows for mapping retrospective distributions of human settlement and colonial land development at unprecedented spatial and temporal granularity (i.e. 250 m spatial and 5-year temporal resolution), and extends across an unmatched time period These data help overcome several fundamental temporal and spatial limitations in data sources widely used by the Earth system science community such as the Global Human Settlement Layer (GHSL; Pesaresi et al, 2013), the World Settlement Footprint Evolution dataset (Marconcini et al, 2020), the National Land Cover Database (NLCD; Homer et al, 2007), the Global Rural-Urban Mapping Project (GRUMP; CIESIN, 2004), or multi-temporal population datasets (e.g. Gridded Population of the World (GPW; Balk and Yetman, 2004), WorldPop (Tatem, 2017), GHS-POP (Freire et al, 2016), or LandScan (Dobson et al, 2000)) (see an overview in Leyk et al, 2019), as well as sparse and more computationally expensive and labour-intensive alternatives such as historical and archaeological records (Reba et al, 2016; Hedefalk et al, 2017; Ostafin et al, 2020; Lieskovský et al, 2018), georeferenced social science data (Kugler et al, 2019), data extracted from historical maps (Uhl et al, 2019; Kaim et al, 2016), or model-based inferences (Klein Goldewijk et al, 2011; Sohl et al, 2016).

Main data products

Source data and data processing

Validation data

Contemporary US-wide building footprint data

Multi-temporal building footprint data

Multi-temporal US census housing statistics

Data on public housing and buildings

Data uncertainty and validation

Data incompleteness

Locational uncertainty

Analysing spatial generalization effects

Positional accuracy across multiple spatial resolutions