DOI: 10.5061/dryad.tb2rbp0d1
title: Incorporating infrastructure and vehicle technology requirements, changes in demand, and Decarbonization policies’ considerations into freight planning

description: This report develops an equitable and sustainable freight-oriented LU
 methodology to support future planning activities, enabling the
 integration of freight activity across urban, suburban, and rural areas
 and facilitating the transition of heavy- and medium-duty vehicles toward
 zero-emission. The methods include a literature review to identify freight
 sustainable strategies, policy analysis at different scales,
 characterization of local context, and demand/supply patterns. The
 latter examines the spatial distribution and land use
 characteristics of freight facilities and retail/service sectors in the
 Sacramento region to inform sustainable and equitable planning strategies.
 This analysis identifies co-location patterns, accessibility gaps, and
 sectoral interactions using a multi-dimensional approach integrating
 spatial clustering, distance analysis, population-employment dynamics, and
 environmental burdens. Data sources include LODES, ACS, CalEnviroScreen,
 and OpenStreetMap, alongside geospatial tools in R. The findings suggest
 the need for targeted interventions to address potential conflicts,
 service deserts, and environmental justice concerns. The study proposes
 actionable strategies for planners to support balanced economic
 development and improve access to essential services.
description: The proposed framework comprises four main steps. The first
 focuses on characterizing the freight context based on local, State, and
 federal decarbonization plans, equity policies, land use plans, and labor
 conditions. The second step analyzes urban, suburban, and rural freight
 distribution patterns based on supply and demand characteristics. The
 third step analyzes potential freight-efficient LU and transportation
 initiatives that can be implemented locally (including decarbonization,
 social, and environmental policies). Finally, step four develops
 sustainable and equitable freight LU methodology considering impacts on
 environmental justice, GHG emissions, labor, and social justice. The case
 study for implementing this methodology is the Sacramento region,
 California.
description: # Incorporating infrastructure and vehicle technology requirements,
 changes in demand, and Decarbonization policies’ considerations into
 freight planning Dataset DOI:
 [10.5061/dryad.tb2rbp0d1](10.5061/dryad.tb2rbp0d1) ## Description of the
 data and file structure #### **Data was collected for the project:**
 Incorporating infrastructure and vehicle technology requirements, changes
 in demand, and decarbonization policies considerations into freight ###
 Files and variables #### File: Deliverables.zip **Description:**
 Deliverables.zip contains five files described below. List of deliverables
 included in the ZIP file: 1. synthetic_aggregated_23.gpkg: This file
 contains an aggregation of the 2023 synthetic population, derived from the
 analysis, and is organized by census tract, including shopping
 characteristics and census geometry. Variables: * GEOID: Numeric codes
 that uniquely identify a census tract geographic area. * NAME: Combination
 of census tract number, county, and state. * total_pop: Number of
 inhabitants per census tract. * shop_in_store: Number of in-store shoppers
 per census tract. * shop_online: Number of online shoppers per census
 tract. * pct_instore: Percentage of in-store shoppers per census tract. *
 pct_online: Percentage of online shoppers per census tract. * gi_store:
 Local Getis-Ord Gi∗ value used to identify statistically significant
 spatial clusters of high or low values for in-store purchases. *
 gi_online: Local Getis-Ord Gi∗ value used to identify statistically
 significant spatial clusters of high or low values for online purchases. *
 demand_capita: Delivery Demand per Capita * geometry: Polygon geometry per
 census tract. 2. Store_shopping_emissions_map.gpkg: This dataset contains
 the estimation of CO2, NOx, and PM25 for in-store shopping assigned to the
 origin of the shopping trip. Data was collected from the EMFAC2021
 software by the California Air Resources Board. Data is aggregated by
 census tract, considering the years 2017 and 2023. Variables: * GEOID:
 Combination of census tract number, county, and state. * estimated_CO2:
 CO2 estimation for the synthetic population and aggregated by census
 tract. * estimated_NOx: NOx estimation for the synthetic population and
 aggregated by census tract. * estimated_PM25: PM25 estimation for the
 synthetic population and aggregated by census tract. * NAME: Combination
 of census tract number, county, and state. * total_pop: Number of
 inhabitants per census tract. * geometry: Polygon geometry per census
 tract. 3. Combined_services.gpkg: This file contains a combination of all
 the retail locations with the category, description and geometry. The data
 in this file was collected under the Open Data Commons Open Database
 License (ODbL), OpenStreetMap Foundation (OSMF). Data was retrieved from
 January to March 2025. Variables: * fid: Feature Identifier * oms_id:
 OpenStreetMap’s identifier. * category: Type of service provided by the
 retail, which includes restaurants, accommodation, food service,
 technical, health. * retail: Type of service provided by the retail but
 more disaggregated. * geometry: Polygon geometry of the building area. 4.
 Merge_facilities_buildings.gpkg: This file contains industry buildings
 with a sample of freight facilities, including characteristics such as
 classification, height, and building area. The data in this file was
 collected under the Open Data Commons Open Database License (ODbL),
 OpenStreetMap Foundation (OSMF). Data was retrieved from January to March
 2025. Data was complemented using the Overture Maps Dataset from Amazon S3
 and Microsoft Azure Blob Storage using an API key. Variables: * fid:
 Feature Identifier * Search_Type: Term used for searching the facility in
 OpenStreetMap. No classified means the facility was identified from other
 sources, such as Overture. * lon: longitude finder. * lat: latitude
 finder. * Classification: Classification of facilities based on two-digit
 North American Industry Classification System (NAICS). * height: building
 height information in meters. * area: building area in square degrees. 5.
 Facilities_year.csv: This file contains a sample of freight facilities
 with the year of construction, name, address, type of facility, and
 geometry. The data in this file was collected under the Open Data Commons
 Open Database License (ODbL) OpenStreetMap Foundation (OSMF). Data was
 retrieved from January to March 2025. Imagery data from Google Earth
 Studio. © Google was used as a complementary source of information to
 verify and complete missing data. Variables: * Facility_Name: Name of the
 freight facility. * Address: Address of the freight facility. *
 Search_Type: Classification based on two-digit North American Industry
 Classification System (NAICS). * lon: longitude finder. * lat: latitude
 finder. * Year: year of the facility's starting operation. ##
 Code/software To read the .csv data, users can open it directly in a
 spreadsheet program like Excel or Google Sheets. Users can use various GIS
 software and programming languages, including ArcGIS, QGIS, or Manifold,
 to read a shapefile within a GeoPackage (.gpkg) file. They can also read
 shapefiles using programming languages such as Python and R. ## Access
 information Data was derived from the following sources: * American Time
 Use Survey (ATUS) - American Community Survey (ACS) * National Household
 Travel Survey (NHTS) - Emissions Inventory - EMFAC2021 software *
 OpenStreetMap (OSM) Additional information such as ATUS, ACS, or NHTS data
 collected, and synthetic population created, can be provided upon request
 via email. ## Human subjects data This dataset includes human subjects
 data obtained from public-use datasets that have been fully de-identified
 and made publicly available by the original data providers. Specifically:
 *The American Community Survey (ACS) and American Time Use Survey (ATUS)
 data were accessed via the IPUMS USA platform using an API. IPUMS USA
 provides anonymized, public-use microdata that are de-identified in
 accordance with U.S. Census Bureau disclosure standards. All direct
 identifiers have been removed, and statistical techniques have been
 applied to reduce the risk of re-identification. *The Longitudinal
 Employer-Household Dynamics (LEHD) data were accessed via an API and
 consist of aggregated or anonymized public-use datasets from the U.S.
 Census Bureau. These data comply with strict confidentiality protocols
 under Title 13 of the U.S. Code. *The California Communities Environmental
 Health Screening Tool (CalEnviroScreen 4.0) includes environmental,
 health, and demographic indicators at the census tract level. The data are
 aggregated and do not contain any personally identifiable information
 (PII). *The California Energy Commission, California Air Resources Board
 (EMFAC tool), and National Household Travel Survey (NHTS) datasets were
 downloaded from public sources. These datasets are anonymized and do not
 include individual-level identifiers. *The County Business Patterns (CBP)
 and Economic Census (ECNSVY) data were obtained from the U.S. Census
 Bureau's public data portals. These are aggregated at the business
 and geographic level and do not include PII. No personally identifiable
 information (PII) or protected health information (PHI) is included in the
 data deposited to Dryad. All data used in this submission are in the
 public domain or provided under terms consistent with a CC0 license, and
 have been de-identified by the original data providers in accordance with
 legal and ethical standards for public data release. No additional
 participant consent was required beyond the terms established by the
 original data custodians.