Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

A collection of populated places and 2010 census population estimates. for unincorporated areas, these estimates are based on AGRC's subjective assignment of census blocks to named places or to an other/unassigned generic holder.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

A collection of populated places and 2010 census population estimates. for unincorporated areas, these estimates are based on AGRC's subjective assignment of census blocks to named places or to an other/unassigned generic holder.

Comprehensive demographic dataset for Jordan Industrial Center, West Jordan, UT, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

U.S. Census Bureau QuickFacts statistics for Beaver County, Utah. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

Demographic.PopBlockAreas2010_Approx contains areas of census blocks that just represent populated areas. This dataset was created by AGRC using the original 2010 census blocks. The blocks were cut when necessary to only cover residential and other developed areas. This was done using mainly aerial imagery and is just an approximation. This dataset was updated in 2015 using new aerial photography and address point locations.

Abstract

This dataset includes all individuals from the 1860 US census.

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Documentation

This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.

The official enumeration day of the 1860 census was 1 June 1860. The main goal of an early census like the 1860 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.

Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT

Last Update: 02/2021This datasets was was downloaded from the 2020 Census Redistricting Data (P.L. 94-171) page. All 2020 census boundaries are current to January 1, 2020. The Census Bureau will release the first set of corresponding demographic data in September 2021 (the 2020 Census Redistricting P.L. 94-171 Summary Files). Following that release, AGRC will append the demographic data to the existing 2020 geographies served on this page.Census tracts are small, relatively permanent subdivisions of a county designed to present and compare statistical data for areas of roughly equal population. Census tracts generally contain between 1,200 and 8,000 people, with an optimum population of 4,000. A census tract is spatially smaller in a higher-density area and larger in a more sparsely populated area. In higher-density areas, tracts can be considered approximately “neighborhood” sized.Visit the SGID 2020 Census data pagefor more information.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

This data set contains public health indicators at the census tract level. These data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9).

Premise: The domestication of wild plant species can begin with gathering and transport of propagules by Indigenous peoples. The effect on genomic composition, especially in clonal, self-incompatible perennials would be instantaneous and drastic with respect to new, anthropogenic populations subsequently established. Reductions in genetic diversity and mating capability would be symptomatic and the presence of unique alleles and genetic sequences would reveal the origins and ancestry of populations associated with archaeological sites. The current distribution of the Four Corners potato, Solanum jamesii Torr. in the Southwestern USA, may thus reflect the early stages of a domestication process that began with tuber transport. Methods: Herein genetic sequencing (GBS) data are used to further examine the hypothesis of domestication in this culturally significant species by sampling 25 archaeological and non-archaeological populations. Key Results: Archaeological populations from Utah, Colorado, and northern Arizona have lower levels of polymorphic loci, unique alleles, and heterozygosity than non-archaeological populations from the Mogollon Region of central Arizona and New Mexico. Principle components analysis, Fst values, and structure analysis revealed that genetic relationships among archaeological populations did not correspond to geographic proximity. Populations in Escalante, Utah were related to those on the Mogollon Rim (400 km south) and had multiple origins and significant disjunctions with those in Bears Ears, Chaco Canyon, and Mesa Verde. Conclusions: Movement of tubers from the Mogollon Region may have occurred many times and in multiple directions during the past, resulting in the complex genetic patterns seen in populations from across the Four Corners Region. Methods Selection of sample sites – The S. jamesii accession database contains a total of 169 extant occurrences from across the entire range of the species (Bamberg et al., 2003, with updates), of which 36 are associated with ancient sites (e.g. pueblos, granaries, habitation structures, kivas). These were divided into three latitudinal strata, each containing roughly a third of the total occurrences. From each stratum, a total of 12 primary and secondary candidate sites were randomly chosen. Candidate sites were surveyed in 2019, 2020, and 2021, and depending on monsoon rainfall, sampled for DNA analysis (in dry years a secondary site might be substituted for a primary site if plants were not present in the latter). In all we were able to obtain leaf samples and site metadata from 14 archaeological (A) and 11 non-archaeological (NA) populations (Fig. 1 and Data available from Dryad at https://doi.org/10.5061/dryad.cz8w9gj9w). Field sampling – Leaf samples of +30 individuals (optimized by Bamberg and del Rio, 2004) were obtained from multiple (6 to 8) widely separated patches in each of the 25 study populations of S. jamesii. There were placed in individual paper tea bags and immediately submerged in silicone gel and/or powder to desiccate. Each sampled individual was geo-referenced with sub-meter GPS (Eos Arrow 100 GNSS, Anatum GeoMobile Solutions, Portland, Oregon, USA), and notes taken on habitat and archeological features (if present within a 300m radius). Sequencing DNA extraction and GBS library construction – Plant DNA was extracted from tissue for each of the samples collected in the collecting trips using a QIACube HT (Qiagen, Germantown, Maryland, USA), an automated extraction robot, which makes high throughput extractions in 96-well plates. These DNA samples were then utilized for Genotyping by Sequencing (GBS), using 48- and 96-plex plates. The first step was to create a reduced representation of the genomes through restriction endonucleases. This cleaved genomic DNA into manageable fragments for PCR and sequencing. Previous work in Solanum jamesii determined that the enzyme combination of PstI and BfaI was the most effective for GBS (Bamberg et al., 2021). After the enzymatic cleavage the sticky ends of DNA were used as anchors for the ligation of barcoded adaptors that served (a) to identify each sample individually from the others during pooled DNA sequencing, (b) as primer binding sites for PCR amplification of the DNA before sequencing, and (c) to adhere other sequencing instrument specific adaptors. After barcode ligation the DNA was amplified via PCR, and then purified and quantified before sequencing. The sequencing was done using a NovaSeq 6000 Sequencer (Illumina, San Diego, California, USA), at the University of Wisconsin-Madison Biotechnology Center (UWBC, https://www. biotech.wisc.edu/). GBS sequence analysis and SNP discovery – To perform the sequencing, reads having low-quality, unidentified bases, and parts of the adapter sequences were eliminated using computational pipelines developed at the Bioinformatics Resource Center (BRC), which is part of the Advanced Genome Analysis Resource unit of the UWBC. The software Skewer (Jiang et al., 2014) was applied to pre-process the raw fastq data files, removing remaining sequences of adapters and/or primers from reads used for sequencing. The reads found to be trimmed too short with poor quality, and those that did not meet minimum sequence length of 150 bp were discarded from downstream analysis. This process recovers true targeted DNA sequences. Reads were then checked for quality using FastQC; each sequence was recognized by its unique barcode adapter (https://sourceforge.net/projects/gbsbarcode/). Raw sequences were processed using Tassel 3.0 and Universal Network Enabled Analysis Kit (UNEAK) pipelines (Lu et al., 2013) for SNP discovery. The bioinformatics computational steps were done on the Unix platform Zcluster at the UWBC. The raw reads were analyzed for quality and sequences were trimmed from the 3’ end of the reads until reaching a Phred quality of 20 (Ewing et al. 1998). These were de-multiplexed using GBSSeqToTagDBPlugin, a step in Tassel GBS Pipeline, generating high-quality reads for each genotype. Genebank accession number GCA_000226075.1 was used as the Reference Genome (Massa et al. 2011) and reads were mapped using Bowtie 2 alignment software (Langmead and Salzberg 2012). SAMtools version 1.3.1 (Li et al., 2009) was used for analyzing and manipulating alignments. A SNP master matrix for each plant collected in the 25 S. jamesii populations was created after the pipeline for SNP discovery and genotype calling using TASSEL reference-based GBS analysis pipeline for SNP calling (Glaubitz et al. 2014). The pipeline for SNP discovery followed standard conditions of depth coverage (minDP≥2), maximum mismatch for alignment (n=3), sufficient data for imputation (i.e. SNP sites with data for >=80% of samples), and minimum Minor Allele Frequency (MAF≥0.05). Data Analysis To determine levels of genetic diversity in the populations, SNP markers were used to calculate genetic relationships among populations and standard estimates of population genetic structure using GenAlex software version 6.5 (Peakall and Smouse, 2006). These include levels of polymorphism, heterozygosity, and number of unique alleles. Principal Component (PC) analyses were also generated with GenAlex to present visual representations of the genetic diversity and differentiation among populations. The purpose of PC was to organize the populations in a space of reduced dimensionality while keeping their genetic identities and divergence, especially in this study where a very large number of individual plants were genotyped. An analysis to determine patterns in the distribution of genetic diversity among the 25 populations was done with the software STRUCTURE 2.3.4 (Pritchard et al., 2000). Hence, a Bayesian approach was used to estimate posterior probabilities to support whether individuals belong to a given K source population by placing individuals into clusters sharing similar patterns of variation. This was done for all the individual plants collected from each population at the sampling sites. An admixture model was assumed for the analyses. The burn-in generation and the MCMC were set to 250,000, with 20 iterations. Delta K, the optimal number of clusters (K) for the sample set, was estimated using the Evanno method (Evanno et al., 2005; Earl and Vonholdt, 2012) through STRUCTURE Harvester. The probability of sampling from the same ramet (“P-clone”) in a population was roughly estimated by tallying the number of individual stems sampled (plants) that had a coefficient of genetic similarity > 0.975 estimated as the complement of Nei’s Genetic Distance coefficient (Nei 1972) and dividing by the total number of stems sampled. Having maximized the distance between samples during collection, this estimate roughly indicates the degree to which cloning (tubering) demographically structures each population.

The table UT- Voter History is part of the dataset Voter History, available at https://columbia.redivis.com/datasets/0n46-6zk4hsfva. It contains 1521328 rows across 342 variables.

See Pederson et al. 2012 (https://doi.org/10.2192/URSUS-D-10-00029.1) for specific methods about sampling design, data collection, processing, and genetic analysis. Resulting capture histories and trap locations were formatted for input into the oSCR package in R:

We formatted the individually identified black bear capture-recapture data from each site into multi-session (3 - 6 years) capture histories that retained spatial information on individual (i) detections at hair collection corrals (j = 16, 25) across sampling occasions (k = 4) such that yijk ~ Bernoulli(pij). The detection model component defines probability of detection of an individual at a particular trap (pij) as a function of distance from the individual’s activity center (si) to that trap having location xj. We used the half-normal model pij = p0 * exp(-dist(xj,si)2/2σ2) where p0 is the baseline encounter probability, x is the location of trap j, s is the activity center of individual i, and σ is the spatial scale parameter, sigma, determining the rate of decrease in detection probability in regards to the distance between xj and si.

We have uploaded a ReadMe document that details each column in the provided trap data frame (tdf) and encounter data frame (edf) .csv files.

This dataset reflects the most current version of Utah county boundaries plus modifications made to correct known issues along the Davis-Weber and Duchesne-Uintah county boundaries. (20111108) and boundary agreement (certified 20120612) between Juab and Millard Counties. And an adjustment between Morgan and Summit Counties (4/3/2013). Minor adjustments were made to align with the Newest PLSS-GCDB layers from BLM (CadNSDIv2), any where from 10 to 50 feet all in non-populated areas (7/18/2014). Minor adjustment between Davis and Weber County (20201105)Data is current through Nov. 15, 2021; Population Figures from 2020 Census. Population Estimates are Null until we get a 2021 update.

Census Tracts for State of Utah with population and housing unit counts

The mortality data in this table have been derived from death certificates in participation with the National Vital Statistics System, and are maintained and provided by the Utah Department of Health, Office of Vital Records. They include virtually all deaths of Utah residents, regardless of where the death occurred. The causes of death were coded using International Classification of Diseases (ICD) codes. The population estimates for years 1980-1999 were produced by the Utah Governor's Office of Planning and Budget (GOPB). For years 2000 and later the population estimates are provided by the National Center for Health Statistics (NCHS) through a collaborative agreement with the U.S. Census Bureau. The leading causes of death is defined by NCHS 50 leading causes.

Survey demographics.

The I-15 Statewide Tool provides a comprehensive overview of the I-15 corridor across the State of Utah. It helps us understand how well I-15 is performing on important Utah transportation values like mobility, safety, and connectivity. The tool also identifies areas where I-15 should be improved to meet Utah’s needs, and provides standards and guidelines that UDOT and other transportation agencies can use to maintain a consistent I-15 experience throughout the state. This map contains Population Projection 2021 to 2050 data for the I-15 Corridor. It is sourced from the Population Projection TAZ data and is considered static. This intermediate map is not intended to be viewed directly, but through the I-15 Tool.This map is a component of the I-15 Population Projections 2024-2050 app and the broader I-15 ToolFor questions on the data, please contact Andrea Moser at AndreaMoser@utah.gov.