skip to main content


Title: Historical road network statistics for core-based statistical areas in the U.S. (1900 - 2010)


Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing. 


Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers.

This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details.

All data created by Keith A. Burghardt, USC Information Sciences Institute, USA


Codebook: these CBSA statistics are stratified by degree of aggregation.

- CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015

- CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015

- CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

- Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015

- Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.


The statistics are the following:

  • msaid: CBSA code
  • id: (if patch statistics) arbitrary int unique to each patch within the CBSA that year
  • year: year of statistics
  • pop: population within all CBSA counties
  • patch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA)
  • patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA)
  • patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA)
  • all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patches
  • all_bupl: Same as above but for all data in 2015 regardless of whether properties were in patches
  • all_bua: Same as above but for all data in 2015 regardless of whether properties were in patches
  • num_nodes: number of nodes (intersections)
  • num_edges: number of edges (roads between intersections)
  • distance: total road length in km
  • k_mean: mean number of undirected roads per intersection
  • k1: fraction of nodes with degree 1
  • k4plus: fraction of nodes with degree 4+
  • bearing: histogram of different bearings between intersections
  • entropy: entropy of bearing histogram
  • mean_local_gridness: Griddedness used in text
  • mean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles)


Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.


References:

Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road Network Evolution in the Urban and Rural  United States Since 1900. Computers, Environment and Urban Systems.

 
more » « less
Award ID(s):
1924670
NSF-PAR ID:
10482847
Author(s) / Creator(s):
;
Publisher / Repository:
figshare
Date Published:
Subject(s) / Keyword(s):
["Geography"]
Format(s):
Medium: X Size: 53707958 Bytes
Size(s):
["53707958 Bytes"]
Sponsoring Org:
National Science Foundation
More Like this
  1. An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing.


    Additionally, there is a CSV file (HISDAC-US_patch_statistics.csv) containing the counts of built-up property records (BUPR), and -locations (BUPL), as well as total building indoor area (BUI) and built-up area (BUA) per CBSA, year, and patch, extraced from the HISDAC-US data (Uhl & Leyk 2018, Uhl et al. 2021). This CSV can be joined to the shapefile (column uid2) by concatenating the columns msaid_year_Id.


    Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers.

    This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details.

    All data created by Johannes H. Uhl, University of Colorado Boulder, USA.

    Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

    References:

    Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road Network Evolution in the Urban and Rural  United States Since 1900. Computers, Environment and Urban Systems.

    Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data  compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI:  https://doi.org/10.1038/sdata.2018.175 

    Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S.,  & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring  200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI:  https://doi.org/10.5194/essd-13-119-2021 

     
    more » « less
  2. These geotiff files represent road network statistics for each core-based statistical area (CBSA) in the conterminous U.S., within grid cells of 1km x 1km. The road network statistics are based on the National transportation dataset (USGS-NTD) v2019.

    These statistics include:

    gridcell_stats_azimuthvariety_1km_all_cbsas.tif: The number of unique road angles (azimuth / orientation) in bins of 10 degrees per 1 sqkm grid cell.

    gridcell_stats_deadendrate_1km_all_cbsas.tif: The proportion of dead ends (nodes of degree 1) of all nodes per 1 sqkm grid cell.

    gridcell_stats_kmroad_1km_all_cbsas.tif: The approximate total road network length per 1 sqkm grid cell. This is based on the road segment length appended to each road segment centroid and may be biased for very long road segments.

    gridcell_stats_meandegree_1km_all_cbsas.tif: The average nodal degree of all nodes per 1 sqkm grid cell.

    gridcell_stats_meangriddedness_1km_all_cbsas.tif: The average griddedness of all nodes per 1 sqkm grid cell.

    gridcell_stats_nodedensity_1km_all_cbsas.tif: The number of nodes per 1 sqkm grid cell.

    gridcell_stats_nodesperkmroad_1km_all_cbsas.tif: The number of nodes per km road within each 1 sqkm grid cell.

    gridcell_stats_firstbuiltup_1km_all_cbsas.tif: The approximate settlement age per 1 sqkm grid cell. This layer is derived from the HISDAC-US First-built-up year (FBUY) layer, which is derived from Zillow's Transaction and Assessment Dataset (ZTRAX). The FBUY data is available here: Leyk, Stefan; Uhl, Johannes H., 2018, "FBUY.tar.gz", Historical settlement composite layer for the U.S. 1810 - 2015, https://doi.org/10.7910/DVN/PKJ90M/BOA5YC, Harvard Dataverse, V2 

    gridcell_stats_1km_all_cbsas_arcmap10.8.mxd: ESRI ArcMap 10.8 MXD file for quick visualization of the gridded surfaces.

    Spatial resolution: 1x1km

    Spatial reference: SR-ORG:7480, USA_Contiguous_Albers_Equal_Area_Conic_USGS_version

    Source data: USGS-NTD, HISDAC-US.

    File format: GeoTIFF.

    Spatial coverage of the road network metrics: All CBSAs in the conterminous U.S.

    Spatial coverage of the "first built-up year" surface: all U.S. counties that are covered by the HISDAC-US  historical settlement layers. This datasets includes around 2,700 U.S.  counties. In the remaining counties, construction year coverage in the  underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is  low. See Leyk & Uhl (2018) for details.

    All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

    References:

    Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road   Network Evolution in the Urban and Rural  United States Since 1900.   Computers, Environment and Urban Systems.

    Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement   data  compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI:  https://doi.org/10.1038/sdata.2018.175 

     
    more » « less
  3. This CSV file contains geometric and topological road network statistics for the majority of counties in the conterminous U.S.

    The underlying road network data is the USGS-NTD v2019.

    These road network data from 2019 were clipped to historical settlement extents obtained from the HISDAC-US dataset 

    Road network statistics are multi-temporal, calculated in time slices for the years: 1810-1900, 1880-1920, 1900-1940, 1920-1960, 1940-1980, 1960-2000, 1980-2015

    The historical built-up areas used to model the historical road networks are derived from historical settlement layers from the  Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the modelling strategy.

    Spatial coverage: all U.S. counties that are covered by the HISDAC-US historical settlement layers. This datasets includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details.

    All data created by Johannes H. Uhl, University of Colorado Boulder, USA.

    Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

    References:

    Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road  Network Evolution in the Urban and Rural  United States Since 1900.  Computers, Environment and Urban Systems.

    Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement  data  compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI:  https://doi.org/10.1038/sdata.2018.175 

    Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D.  S.,  & Balk, D. (2021). Fine-grained, spatiotemporal datasets  measuring  200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI:  https://doi.org/10.5194/essd-13-119-2021 

     
    more » « less
  4. The historical settlement data compilation for Spain (HISDAC-ES) is a geospatial dataset consisting of over 240 gridded surfaces measuring the physical, functional, age-related, and evolutionary characteristics of the Spanish building stock. We scraped, harmonized, and aggregated cadastral building footprint data for Spain, covering over 12,000,000 building footprints including construction year attributes, to create a multi-faceted series of gridded surfaces (GeoTIFF format), describing the evolution of human settlements in Spain from 1900 to 2020, at 100m spatial and 5 years temporal resolution. Also, the dataset contains aggregated characteristics and completeness statistics at the municipality level, in CSV and GeoPackage format.

    !!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.

    File descriptions:

    Datasets are available in three spatial reference systems:

    1. HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.
    2. HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.
    3. HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.
    4. HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.
    5. ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.

    Source data:

    HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:

    • Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilities
    • Bizkaia province: https://web.bizkaia.eus/es/inspirebizkaia
    • Gipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
    • Navarra region: https://inspire.navarra.es/services/BU/wfs
    • Other regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xml
    • Data source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)

    Technical notes:

    Gridded data

    File nomenclature:

    ./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tif

    Regions:

    • all: complete territory of Spain
    • can: Canarian Islands only
    • ibe: Iberic peninsula + Céuta + Melilla

    Projections:

    • laea: Lambert azimuthal equal area (EPSG:3035)
    • regcan: REGCAN95 / UTM zone 28N (EPSG:4083)
    • utm: ETRS89 / UTM zone 30N (EPSG:25830)

    Themes:

    • evolution / evol: multi-temporal physical measurements
    • landuse: multi-temporal building counts per land use (i.e., building function) class
    • physical / phys: physical building characteristics in 2020
    • temporal / temp: temporal characteristics (construction year statistics)

    Variables: evolution

    • budens: building density (count per grid cell area)
    • bufa: building footprint area
    • deva: developed area (any grid cell containing at least one building)
    • resbufa: residential building footprint area
    • resbia: residential building indoor area

    Variables: physical

    • bia: building indoor area
    • bufa: building footprint area
    • bunits: number of building units
    • dwel: number of dwellings

    Variables: temporal

    • mincoy: minimum construction year per grid cell
    • maxcoy: minimum construction year per grid cell
    • meancoy: mean construction year per grid cell
    • medcoy: median construction year per grid cell
    • modecoy: mode (most frequent) construction year per grid cell
    • varcoy: variety of construction years per grid cell

    Variable: landuse

    Counts of buildings per grid cell and land use type.

    Municipality-level data

    • hisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.
    • hisdac_es_municipality_stats_multitemporal_wideform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in wide form. Note that a value of 0 for the year suffix denotes the statistics for records without construction year information.
    • hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.

    Column names for the completeness statistics tables:

    • NATCODE: National municipality identifier*
    • num_total: number of buildings per munic
    • perc_bymiss: Percentage of buildings with missing built year (construction year)
    • perc_lumiss: Percentage of buildings with missing landuse attribute
    • perc_luother: Percentage of buildings with landuse type "other"
    • perc_num_floors_miss: Percentage of buildings without valid number of floors attribute
    • perc_num_dwel_miss: Percentage of buildings without valid number of dwellings attribute
    • perc_num_bunits_miss: Percentage of buildings without valid number of building units attribute
    • perc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attribute
    • perc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attribute

    The same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).

    *From the NATCODE, other regional identifiers can be derived as follows:

    • NATCODE: 34 01 04 04001
    • Country: 34
    • Comunidad autónoma (CA_CODE): 01
    • Province (PROV_CODE): 04
    • LAU code: 04001 (province + municipality code)
     
    more » « less
  5. The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally.

    The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality.

    Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study.

    SPDLC data will be released to the public two years after data is collected; Waves 1 and 2 are currently publicly available. Wave 3 will be publicly available in October 2023, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available. Response Rates: Of the 1,157 parents who participated in Wave 1, 828 (72%) also participated in the Wave 2 study. Presence of Common Scales: The following established scales are included in the survey:
    • Self-Efficacy, adapted from Pearlin's mastery scale (Pearlin et al., 1981) and the Rosenberg self-esteem scale (Rosenberg, 2015) and taken from the American Changing Lives Survey
    • Communication with Partner, taken from the Marriage and Relationship Survey (Lichter & Carmalt, 2009)
    • Gender Attitudes, taken from the National Survey of Families and Households (Sweet & Bumpass, 1996)
    • Depressive Symptoms (CES-D-10)
    • Stress, measured using Cohen's Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983)
    Full details about these scales and all other items included in the survey can be found in the user guide and codebook
    The second wave of the SPDLC was fielded in November 2020 in two stages. In the first stage, all parents who participated in W1 of the SPDLC and who continued to reside in the United States were re-contacted and asked to participate in a follow-up survey. The W2 survey was posted on Prolific, and messages were sent via Prolific’s messaging system to all previous participants. Multiple follow-up messages were sent in an attempt to increase response rates to the follow-up survey. Of the 1,157 respondents who completed the W1 survey, 873 at least started the W2 survey. Data quality checks were employed in line with best practices for online surveys (e.g., removing respondents who did not complete most of the survey or who did not pass the attention filters). After data quality checks, 5.2% of respondents were removed from the sample, resulting in a final sample size of 828 parents (a response rate of 72%).

    In the second stage, a new sample of parents was recruited. New parents had to meet the same sampling criteria as in W1 (be at least 18 years old, reside in the United States, reside with a romantic partner, and be a parent living with at least one biological child). Also similar to the W1 procedures, we oversampled men, Black individuals, individuals who did not complete college, and individuals who identified as politically conservative to increase sample diversity. A total of 1,207 parents participated in the W2 survey. Data quality checks led to the removal of 5.7% of the respondents, resulting in a final sample size of new respondents at Wave 2 of 1,138 parents.

    In both stages, participants were informed that the survey would take approximately 20 minutes to complete. All panelists were provided monetary compensation in line with Prolific’s compensation guidelines, which require that all participants earn above minimum wage for their time participating in studies.
    To be included in SPDLC, respondents had to meet the following sampling criteria at the time they enter the study: (a) be at least 18 years old, (b) reside in the United States, (c) reside with a romantic partner (i.e., be married or cohabiting), and (d) be a parent living with at least one biological child. Follow-up respondents must be at least 18 years old and reside in the United States, but may experience changes in relationship and resident parent statuses. Smallest Geographic Unit: U.S. State

    This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. In accordance with this license, all users of these data must give appropriate credit to the authors in any papers, presentations, books, or other works that use the data. A suggested citation to provide attribution for these data is included below:            

    Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  

    To help provide estimates that are more representative of U.S. partnered parents, the SPDLC includes sampling weights. Weights can be included in statistical analyses to make estimates from the SPDLC sample representative of U.S. parents who reside with a romantic partner (married or cohabiting) and a child aged 18 or younger based on age, race/ethnicity, and gender. National estimates for the age, racial/ethnic, and gender profile of U.S. partnered parents were obtained using data from the 2020 Current Population Survey (CPS). Weights were calculated using an iterative raking method, such that the full sample in each data file matches the nationally representative CPS data in regard to the gender, age, and racial/ethnic distributions within the data. This variable is labeled CPSweightW2 in the Wave 2 dataset, and CPSweightLW2 in the longitudinal dataset (which includes Waves 1 and 2). There is not a weight variable included in the W1-W2 repeated cross-section data file.
     
    more » « less