In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

The California School Campus Database (CSCD) is now available for all public schools and colleges/universities in California.CSCD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Each is accurately mapped at the assessor parcel level. CSCD is the first statewide database of this information and is available for use without restriction.PURPOSEWhile data is available from the California Department of Education (CDE) at a point level, the data is simplified and often inaccurate.CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data is also provided for community colleges and university lands as of the 2018 release.CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes. It should not be used as the basis for official regulatory, legal, or other such governmental actions unless reviewed by the user and deemed appropriate for their use. See the user manual for more information.Link to California School Campus Database.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

The Chicago Historic Resources Survey (CHRS), completed in 1995, was a decade-long research effort by the City of Chicago to analyze the historic and architectural importance of all buildings, objects, structures, and sites constructed in the city prior to 1940. During 12 years of field work and follow-up research that started in 1983, CHRS surveyors identified approximately 9,900 properties which were considered to have some historic or architectural importance. Please note that this CHRS dataset is limited and does not include the entire survey: A color-coded ranking system was used to identify historic and architectural significance relative to age, degree of external physical integrity, and level of possible significance. This dataset only includes buildings identified with the two highest color codes: "Red" and "Orange." Buildings and structures coded "Red" or "Orange" (unless designated as a Chicago Landmark or located within a Chicago Landmark District) are subject to the City of Chicago’s Demolition-Delay Ordinance (link to: http://www.cityofchicago.org/city/en/depts/dcd/supp_info/demolition_delay.html), adopted by City Council in 2003. Only buildings are included in this dataset; structures and objects such as bridges, park structures, monuments and mausoleums, generally are not represented. Likewise, garages, coach houses, and other secondary structures associated with a building may not be consistently depicted or color-coded. If an “Orange”- or “Red”-rated building was demolished after 2008, it may still appear in the map. The CHRS occasionally rated only part of a building or part of a group of joined buildings as “Orange” or “Red;” however the entire building or group of joined buildings may be incorrectly identified as “Orange” or “Red.” Additional information about the CHRS is available at www.cityofchicago.org/Landmarks/ or by contacting the Historic Preservation Division at (312) 744-3200. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

VDEQ Spring SITESThe VDEQ Spring SITES database contains data describing the geographic locations and site attributes of natural springs throughout the commonwealth. This data coverage continues to evolve and contains only spring locations known to exist with a reasonable degree of certainty on the date of publication. The dataset does not replace site specific inventorying or receptor surveys but can be used as a starting point. VDEQ's initial geospatial dataset of approximately 325 springs was formed in 2008 by digitizing historical spring information sheets created by State Water Control Board geologists in the 1970s through early 1990s. Additional data has been consolidated from the EPA STORET database, the U.S. Geological Survey's Ground Water Site Inventory (GWSI) and Geographic Names Inventory System (GNIS), the Virginia Department of Health SDWIS database, the Virginia DEQ Virginia Water Use Data Set (VWUDS), the Commonwealth of Virginia Division of Water Resources and Power Bulletin No. 1: "Springs of Virginia" by Collins et al., 1930 as well as several VDWR&P Surface Water Supply bulletins from the 1940's - 1950's. A 1992 Virginia Department of Game and Inland Fisheries / Virginia Tech sponsored study by Helfrich et al. titled "Evaluation of the Natural Springs of Virginia: Fisheries Management Implications", a 2004 Rockbridge County groundwater resources report written by Frits van der Leeden, and several smaller datasets from consultants and citizens were evaluated and added to the database when confidence in locational accuracy was high or could be verified with aerial or LIDAR imagery. Significant contributions have been made throughout the years by VDEQ Groundwater Characterization staff site visits as well as other geologists working in the region including: Matt Heller at Virginia Division of Geology and Mineral Resources (VDMME), Wil Orndorff at the Virginia Department of Conservation and Recreation Karst Program (VDCR), and David Nelms and Dan Doctor of the U.S. Geological Survey (USGS). Substantial effort has been made to improve locational accuracy and remove duplication present between data sources. Hundreds of spring locations that were originally obtained using topographic maps or unknown methods were updated to sub-meter locational accuracy using post-processed differential GPS (PPGPS) and through the use of several generations of aerial imagery (2002-2017) obtained from Virginia's Geographic Information Network (VGIN) and 1-meter LIDAR, where available. Scores of new spring locations were also obtained by systematic quadrangle by quadrangle analysis in areas of the Shenandoah Valley where 1-meter LIDAR datasets where obtained from the U.S. Geological Survey. Future improvements to the dataset will result when statewide 1-meter LIDAR datasets becomes available and through continued field work by DEQ staff and other contributors working in the region. Please do not hesitate to contact the author to correct mistakes or to contribute to the database.VDEQ_Springs_FIELD_MEASUREMENTSThe VDEQ Spring FIELD MEASUREMENTS database contains data describing field derived physio-chemical properties of spring discharges measured throughout the Commonwealth of Virginia. Field visits compiled in this dataset were performed from 1928 to 2019 by geologists with the State Water Control Board, the Virginia Division of Water and Power, the Virginia Department of Environmental Quality, and the U.S. Geological Survey with contributions from other sources as noted. Values of -9999 indicate that measurements were not performed for the referenced parameter. Please do not hesitate to contact the author to add data to the database or correct errors.VDEQ_Springs_WQThe VDEQ_Spring_WQ database is a geodatabase containing groundwater sample information collected from springs throughout Virginia. Sample specific information include: location and site information, measured field parameters, and lab verified quantifications of major ionic concentrations, trace element concentrations, nutrient concentrations, and radiological data. The VDEQ_Spring_WQ database is a subset of the VDEQ GWCHEM database which is a flat-file geodatabase containing groundwater sample information from groundwater wells and springs throughout Virginia. Sample information has been correlated via DEQ Well # and projected using coordinates in VDEQ_Spring_SITES database. The GWCHEM database is comprised of historic groundwater sample data originally archived in the United States Geological Survey (USGS) National Water Information System (NWIS) and the Environmental Protection Agency (EPA) Storage and Retrieval (STORET) data warehouse. Archived STORET data originated as groundwater sample data collected and uploaded by Virginia State Water Control Board Personnel. While groundwater sample data in the STORET data warehouse are static, new groundwater sample data are periodically uploaded to NWIS and spring laboratory WQ data reflect NWIS downloaded on 9/30/2019. Recent groundwater sample data collected by Virginia Department of Environmental Quality (DEQ) personnel as part of the Ambient Groundwater Sampling Program are entered into the database as lab results are made available by the Division of Consolidated Laboratory Services (DCLS). When possible, charge balances were calculated for samples with reported values for major ions including (at a minimum) calcium, magnesium, potassium, sodium, bicarbonate, chloride, and sulfate. Reported values for Nitrate as N, carbonate, and fluoride were included in the charge balance calculation when available. Field determined values for bicarbonate and carbonate were used in the charge balance calculation when available. For much of the legacy DEQ groundwater sample data, bicarbonate values were derived from lab reported values of alkalinity (as mg/CaCO3) under the assumption that there was no contribution by carbonate to the reported alkalinity value. Charge balance values are reported in the "Charge Balance" column of the GWCHEM geodatabase. The closer the charge balance value is to unity (1), the lower the assumed charge balance error.In order to preserve the numerical capabilities of the database, non- numeric lab qualifiers were given the following numeric identifiers:- (minus sign) = less than the concentration specified to the right of the sign-11110 = estimated-22220 = presence verified but not quantified-33330 = radchem non-detect, below sslc-4440 = analyzed for but not detected-55550 = greater than the concentration to the right of the zero-66660 = sample held beyond normal holding time-77770 = quality control failure. Data not valid.-88880 = sample held beyond normal holding time. Sample analyzed for but not detected. Value stored is limit of detection for proces in use.-11120 = Value reported is less than the criteria of detection.-9999 = no data (parameter not quantified)A more in depth descprition and hydrogeologic analysis of the database can be found hereAn in Depth data fact sheet can be found here

As part of the cultural landscape, administrative toponyms do not only reflect natural and sociocultural phenomena, but also help with related management and naming work. Historically, county-level administrative districts have been stable and basic administrative regions in China, playing a role in the country’s management. We explore the spatio-temporal evolutionary characteristics of the county-level administrative toponyms cultural landscape in China’s eastern plains areas. A Geographical Information System (GIS) analysis, Geo-Informatic Tupu, Kernel Density Estimation, and correlation coefficients were conducted. We constructed a GIS database of county-level administrative toponyms from the Sui dynasty onward using the Northeast China, North China, and Yangtze Plains as examples. We then summarized the spatio-temporal evolutionary characteristics of the county-level administrative toponyms cultural landscape in China’s eastern plains areas. The results indicate that (1) the number of toponyms has roughly increased over time; (2) toponym densities on the three plains are higher than the national average in the corresponding timeframe since the Sui; and (3) county-level administrative toponyms related to mountains and hydrological features accounted for more than 30% of the total in 2010. However, the percentage of county-level administrative toponyms related to natural factors on the three plains has decreased since the Sui. To explore the factors influencing this spatio-temporal evolution, we analyzed the correlations between the toponyms and natural factors and human/social factors. The correlation degree between toponym density and population density is the highest, and that between toponym density and Digital Elevation Model (DEM) the lowest. Temperature changes were important in toponym changes, and population changes have influenced toponym changes over the last 400 years in China.

Table from the American Community Survey (ACS) 5-year series on poverty and employment status related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B23025 Employment Status for the Population 16 years and over, B23024 Poverty Status by Disability Status by Employment Status for the Population 20 to 64 years, B17010 Poverty Status of Families by Family Type by Presence of Related Children under 18 years, C17002 Ratio of Income to Poverty Level in the Past 12 Months. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B23025, B23024, B17010, C17002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

This dataset contains maps showing the principal attributes of tides around the Australian coast. It has been derived from data published in the Australian National Tide Tables.

Format: shapefile.

Quality - Scope: Dataset. External accuracy: +/- one degree. Non Quantitative accuracy: Data are assumed to be correct. Three datasets describe tidal information around Australia:

Cover_Name, Item_Name, Item_Description:

TIDEMAX, MAX_TIDE_(M), Maximum tidal range in metres.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to geographics using the WGS84 datum and spheroid, to be compatible for the Australian Coastal Atlas. The digital datsets were attributed using the information held in the legend (.key) files.

CSIRO: All CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

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.

Overview

This dataset contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).

Preprocessing/Formatting

It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).

• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)

• A single street closure may span multiple segments of a street.

• The street closure permit refers to all the component line segments.

• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.

The roadway_id field is a unique GIS line segment representing the aforementioned segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.

The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.

City teams that use this data requested that each segment of each street closure permit be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows. Thus, the values in most fields of the three records are identical.

Each row has the fields segment_num and total_segments which detail the relationship of each record, and its corresponding permit, according to street segment. The above example produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.

The geometry field consists of string values of lat/long coordinates, which can be used to map the street segments.

All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.

Known Uses

These data are used by DOMI to track the status of street closures (and associated permits).

Further Documentation and Resources

An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits

Commonage GIS Dataset. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Commonage Framework Planning was a joint initiative between the National Parks and Wildlife Service and the Department of Agriculture and Food. Teams combining agricultural and ecological skills to assess the sustainable use of these areas have surveyed all known commonage areas in Ireland.

To date in excess of 4,400 plans have been prepared, covering more than 440,000 hectares. Where necessary, destocking (removal of some of the stock kept on commonage) was prescribed to ensure recovery of the vegetation. These plans have been implemented through REPS, AEOS and the NPWS Farm Plan Scheme, as relevant, from 1999 - 2012.

A commitment has been made to monitor the condition of commonages to demonstrate, in particular, that initiatives are delivering recovery in overgrazed areas and that undergrazing is not becoming a problem. Ireland also has obligations to monitor the state of SACs containing uplands and peatlands in non-commonage areas. This involves a reassessment of habitats in commonage areas, some of which were assessed as early as 1999, and also non-commonage areas.

Planning teams comprising both agriculturalists and environmentalists have been trained and re-surveys have been completed in commonage blocks in Counties Mayo, Galway, Cork, Kerry, Donegal, Sligo, Leitrim, Tipperary, Limerick and Louth between 2004 and 2010. Monitoring reports have been forwarded to the EU Commission highlighting the findings and trends. Additional survey work in 2007 focussed on Counties Mayo, Donegal and Kerry. In 2008, all commonage that had a destocking of greater than 50% were re-assessed.

In this context GIS files were set up to describe: - Destocking rates assigned to Agricultural Units - Habitat types and damage categories assigned to Agricultural Sub-Units and - Locations of Base-Stations and habitat types / damage categories recorded at these stations

A review of all the Commonage Framework Plans, setting sustainable stocking rates, will conclude in 2012 and will be communicated to all shareholders by the Department of Agriculture, Food and the Marine. This information is not contained here....

These data were created as part of the National Oceanic and Atmospheric Administration Coastal Services Center's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. See the NOAA Sea Level Rise and Coastal Flooding Impacts Viewer. These data depict the mapping confidence of the associated Sea Level Rise inundation data, for the sea level rise amount specified. Areas that have a low degree of confidence, or high uncertainty, represent locations that may be mapped correctly (either as inundated or dry) less than 8 out of 10 times. Areas that have a high degree of confidence, or low uncertainty, represent locations that will be correctly mapped (either as inundated or dry) more than 8 out of 10 times or that there is an 80 percent degree of confidence that these areas are correctly mapped. Areas mapped as dry (no inundation) with a high confidence or low uncertainty are coded as 0. Areas mapped as dry or wet with a low confidence or high uncertainty are coded as 1. Areas mapped as wet (inundation) with a high confidence or low uncertainty are coded as 2. The NOAA Coastal Services Center has tentatively adopted an 80 percent rank (as either inundated or not inundated) as the zone of relative confidence. The use of 80 percent has no special significance but is a commonly used rule of thumb measure to describe economic systems (Epstein and Axtell, 1996). In short, the method includes the uncertainty in the lidar derived elevation data (root mean square error, or RMSE) and the uncertainty in the modeled tidal surface from the NOAA VDATUM model (RMSE). This uncertainty is combined and mapped to show that the inundation depicted in this data is not really a hard line, but rather a zone with greater and lesser chances of getting wet. For a detailed description of the confidence level and its computation, please see the Mapping Inundation Uncertainty document.

This layer serves as the authoritative geographic data source for all school district area boundaries in California. School districts are single purpose governmental units that operate schools and provide public educational services to residents within geographically defined areas. Agencies considered school districts that do not use geographically defined service areas to determine enrollment are excluded from this data set. In order to view districts represented as point locations, please see the "California School District Offices" layer. The school districts in this layer are enriched with additional district-level attribute information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.School districts are categorized as either elementary (primary), high (secondary) or unified based on the general grade range of the schools operated by the district. Elementary school districts provide education to the lower grade/age levels and the high school districts provide education to the upper grade/age levels while unified school districts provide education to all grade/age levels in their service areas. Boundaries for the elementary, high and unified school district layers are combined into a single file. The resulting composite layer includes areas of overlapping boundaries since elementary and high school districts each serve a different grade range of students within the same territory. The 'DistrictType' field can be used to filter and display districts separately by type.Boundary lines are maintained by the California Department of Education (CDE) and are effective in the 2023-24 academic year . The CDE works collaboratively with the US Census Bureau to update and maintain boundary information as part of the federal School District Review Program (SDRP). The Census Bureau uses these school district boundaries to develop annual estimates of children in poverty to help the U.S. Department of Education determine the annual allocation of Title I funding to states and school districts. The National Center for Education Statistics (NCES) also uses the school district boundaries to develop a broad collection of district-level demographic estimates from the Census Bureau’s American Community Survey (ACS).The school district enrollment and demographic information are based on student enrollment counts collected on Fall Census Day (first Wednesday in October) in the 2023-24 academic year. These data elements are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website https://www.cde.ca.gov/ds.

This dataset is OBSOLETE as of 12/3/2024 and will be removed from ArcGIS Online on 12/3/2025.An updated version of this dataset is available at Land Use FY2024.This data set derives from several sources, and is updated annually with data current through July 1 of the reported year. The primary source is a data dump from the VISION assessing data system, which provided data up to date as of January 1, 2012, and is supplemented by information from subsequent building permits and Development Logs. (Use codes provided by this system combine aspects of land use, tax status, and condominium status. In an effort to clarify land use type the data has been cleaned and subdivided to break the original use code into several different fields.) The data set has further been supplemented and updated with development information provided by building permits issued by the Inspectional Services Department and from data found in the Development Log publication. Information from these sources is added to the data set periodically. Land use status is up to date as of the Last Modified date.Differences From “Official” Parcel LayerThe Cambridge GIS system maintains a separate layer of land parcels reflecting up to date subdivision and ownership. The parcel data associated with the Land Use Data set differs from the “official” parcel layer in a number of cases. For that reason this separate parcel layer is provided to work with land use data in a GIS environment. See the Assessing Department’s Parcel layer for the most up-to-date land parcel boundaries.Table of Land Use CodesThe following table lists all land use code found in the data layer:Land Use CodeLand Use DescriptionCategory0101MXD SNGL-FAM-REMixed Use Residential0104MXD TWO-FAM-RESMixed Use Residential0105MXD THREE-FM-REMixed Use Residential0111MXD 4-8-UNIT-APMixed Use Residential0112MXD >8-UNIT-APTMixed Use Residential0121MXD BOARDING-HSMixed Use Residential013MULTIUSE-RESMixed Use Residential031MULTIUSE-COMMixed Use Commercial0340MXD GEN-OFFICEMixed Use Commercial041MULTIUSE-INDMixed Use Industrial0942Higher Ed and Comm MixedMixed Use Education101SNGL-FAM-RESResidential1014SINGLE FAM W/AUResidential104TWO-FAM-RESResidential105THREE-FM-RESResidential106RES-LAND-IMPTransportation1067RES-COV-PKGTransportation1114-8-UNIT-APTResidential112>8-UNIT-APTResidential113ASSISTED-LIVAssisted Living/Boarding House121BOARDING-HSEAssisted Living/Boarding House130RES-DEV-LANDVacant Residential131RES-PDV-LANDVacant Residential132RES-UDV-LANDVacant Residential1322RES-UDV-PARK (OS) LNVacant Residential140CHILD-CARECommercial300HOTELCommercial302INN-RESORTCommercial304NURSING-HOMEHealth316WAREHOUSECommercial323SH-CNTR/MALLCommercial324SUPERMARKETCommercial325RETAIL-STORECommercial326EATING-ESTBLCommercial327RETAIL-CONDOCommercial330AUTO-SALESCommercial331AUTO-SUPPLYCommercial332AUTO-REPAIRCommercial334GAS-STATIONCommercialLand Use CodeLand Use DescriptionCategory335CAR-WASHCommercial336PARKING-GARTransportation337PARKING-LOTTransportation340GEN-OFFICEOffice341BANKCommercial342MEDICAL-OFFCHealth343OFFICE-CONDOOffice345RETAIL-OFFICOffice346INV-OFFICEOffice353FRAT-ORGANIZCommercial362THEATRECommercial370BOWLING-ALLYCommercial375TENNIS-CLUBCommercial390COM-DEV-LANDVacant Commercial391COM-PDV-LANDVacant Commercial392COM-UDV-LANDVacant Commercial3922CRMCL REC LNDVacant Commercial400MANUFACTURNGIndustrial401WAREHOUSEIndustrial404RES-&-DEV-FCOffice/R&D406HIGH-TECHOffice/R&D407CLEAN-MANUFIndustrial409INDUST-CONDOIndustrial413RESRCH IND CNDIndustrial422ELEC GEN PLANTUtility424PUB UTIL REGUtility428GAS-CONTROLUtility430TELE-EXCH-STAUtility440IND-DEV-LANDVacant Industrial442IND-UDV-LANDVacant Industrial920ParklandsPublic Open Space930Government OperationsGovernment Operations934Public SchoolsEducation940Private Pre & Elem SchoolEducation941Private Secondary SchoolEducation942Private CollegeHigher Education9421Private College Res UnitsEducation Residential943Other Educ & Research OrgHigher EducationLand Use CodeLand Use DescriptionCategory953CemeteriesCemetery955Hospitals & Medical OfficHealth956MuseumsHigher Education957Charitable ServicesCharitable/Religious960ReligiousCharitable/Religious971Water UtilityUtility972Road Right of WayTransportation975MBTA/RailroadTransportation9751MBTA/RailroadTransportation995Private Open SpacePrivately-Owned Open SpaceExplore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription ML type: Stringwidth: 16precision: 0 Map-Lot: This a unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

MAP type: Stringwidth: 5precision: 0 This Map portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

LOT type: Stringwidth: 5precision: 0 This is the Lot portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

Location type: Stringwidth: 254precision: 0 In the great majority of cases this is the street address of the parcel as it is recorded in the Registry of Deed record. In instances where edits were made to the base parcel layer the best address available at the time is employed.

LandArea type: Doublewidth: 8precision: 15

LUCode type: Stringwidth: 254precision: 0 The four digit text string in this field indicates the primary usage of a parcel. While the codes are based on the standard Massachusetts assessing land use classification system, they differ in a number of cases; the coding system used here is unique to this data set. Note that other minor uses may occur on a property and, in some cases, tenants may introduce additional uses not reflected here (eg, office space used as a medical office, home based businesses).

LUDesc type: Stringwidth: 254precision: 0 The short description gives more detail about the specific use indicated by the Land Use Code. Most descriptions are taken from the standard Massachusetts assessing land use classification system.

Category type: Stringwidth: 254precision: 0 This broader grouping of land uses can be used to map land use data. You can find the land use data mapped at: https://www.cambridgema.gov/CDD/factsandmaps/mapgalleries/othermaps

ExistUnits type: Doublewidth: 8precision: 15 This value indicates the number of existing residential units as of July 1 of the reported year. A residential unit may be a house, an apartment, a mobile home, a group of rooms or a single room that is occupied (or, if vacant, intended for occupancy) as separate living quarters. This includes units found in apartment style graduate student housing residences and rooms in assisted living facilities and boarding houses are treated as also housing units. The unit count does not include college or graduate student dormitories, nursing home rooms, group homes, or other group quarters living arrangements.

MixedUseTy type: Stringwidth: 254precision: 0 Two flags are used for this field. “Groundfloor” indicates that a commercial use is found on the ground floor of the primary building, and upper floors are used for residential purposes. “Mixed” indicates that two or more uses are found throughout the structure or multiple structures on the parcel, one of which is residential.

GQLodgingH type: Stringwidth: 254precision: 0 A value of “Yes” indicates that the primary use of the property is as a group quarters living arrangement. Group quarters are a place where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents. Group quarters include such places as college residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, and workers’ dormitories.

Most university dormitories are included under the broader higher education land use code, as most dormitories are included in the larger parcels comprising the bulk of higher education campuses.

GradStuden type: Stringwidth: 254precision: 0 A value of “Yes” indicates the parcel is used to house graduate students in apartment style units. Graduate student dormitories are treated as a higher education land use.

CondoFlag type: Stringwidth: 254precision: 0 “Yes” indicates that the parcel is owned as a condominium. Condo properties can include one or more uses, including residential, commercial, and parking. The great majority of such properties in Cambridge are residential only.

TaxStatus type: Stringwidth: 254precision: 0 A value indicates that the parcel is not subject to local property taxes. The following general rules are employed to assign properties to subcategories, though special situations exist in a number of cases.

o Authority: Properties owned the Cambridge Redevelopment Authority and Cambridge Housing Authority. o City: Properties owned by the City of Cambridge or cemetery land owned by the Town of Belmont. o Educ: Includes properties used for education purposes, ranging from pre-schools to university research facilities. (More detail about the level of education can be found using the Land Use Code.) o Federal: Properties owned by the federal government, including the Post Office. Certain properties with assessing data indicating Cambridge Redevelopment Authority ownership are in fact owned by the federal government as part of the Volpe Transportation Research Center and are so treated here. o Other: Nontaxable properties owned by a nonprofit organization and not