This is a geographic analysis showing the number and capacity of Head Start and Great Start centers within 1 Mile Square sectors. Data Driven Detroit set up the square mile sectores and spatially joined the locations of Great Start and Head Start providers (Spring 2016 locations) to each sector. The data are for Oakland, Macomb and Wayne Counties.

Fishnet, Lake Victoria, vector polygon, ~2015 Reference Information and Units: GCS: WGS 1984 (http://spatialreference.org/) Projection: ESRI:102024 (http://spatialreference.org/) Pixel Size: NA Units: NA File Naming Convention: Fishnet_Poly.shp Data Origin: Developed at Salisbury University Sensor: NA Code: NA Data Development/Processing: Data was developed within ArcGIS.

The extent of Arundo donax (common names: Arundo, or giant reed) was digitized using a combination of imagery classification and visual interpretation of Nearmap imagery from September 2014 and April 2022. A total of 80 observations of Arundo locations from the community science website iNaturalist were used to ensure that as much Arundo as possible was correctly mapped. A one ha (100 x 100 m) grid was overlaid on the resulting products and the change in cover between the two time periods was calculated for each grid cell. This layer shows that 1 ha grid.

Abstract

The dataset was created by the Bioregional Assessment Programme. The History Field in this metadata statement describes how this dataset was created.

A 1000 m * 1000m vector grid over the entire Bioregional Assessment Bioregions/Preminary Areas of Extent (using the boundary that is largest) starting at the whole km to ensure grid lines fall exactly on the whole km. The grid is in Australia Albers (GDA94) (EPSG 3577). This grid is intended as the template for standardized assessment units for the following bioregional assessment regions:

Hunter

Namoi

Clarence-Moreton

Galilee

Please note for the Gloucester subregion model a 500m grid ( GUID ) is proposed to be used as the standard assessment unit due to the finer resolution of the output models.

To facilitate processing speed and efficiency each of the above Bioregional Assessments have their own grid and extent created from this master vector grid template, (please see Lineage).

The unique ID field for each grid cell is AUID and starts from 1. The grid also has a column id and row for easy reference and processing.

Purpose

The GRID is an attempt to standardise (where possible) outputs of models from BA assessments and is a whole of BA template for the groundwater and potentially surface water teams of the above mentioned assessment areas.

Dataset History

The Vector grid was generated using the Fishnet tool in ArcGIS. The following fields were added:

AUID - Assessment Unit Unique Id

R001_C001 - A row and column id was calculated using the following python code in the field calculator in ArcGIS where 2685 is the number of rows in the grid and 2324 is the number of columns.

'R' + str(( !OID!-1)/2685).rjust(3, '0') + '_C' + str(( !OID!-1)%2324).rjust(3, '0')

A spatial index was added in ArcGIS 10.1 to increase processing and rendering speed using the Spatial index tool from the ArcToolbox.

The following parameters were used to generate the grid in the Create Fishnet tool in ArcGIS 10.1

Left: -148000

Bottom: -4485000

Fishnet Origin Coordinate

x Coordinate = -148000 Y Coordinate -4485000

Y-Axis Coordinate

X Coordinate -148000 Y Coordinate -4484990

Cell Height - 1000m

Cell Width - 1000m

Number of rows 0

Number of columns 0

Opposite corner: default

Geometry type: Polygon

Y

Dataset Citation

XXXX XXX (2016) BA ALL Assessment Units 1000m 'super set' 20160516_v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/6c1aa99e-c973-4472-b434-756e60667bfa.

The feature is (primarily) a 10' latitude x 10' longitudinal vector grid that defines spatial units for summarizing commercial fishing off the coast of California. The grid cells start at 32° 10.0' N latitude (just below the U.S. / Mexico border) and continue northward to 42° 40.0' N latitude (just north of the California / Oregon border). This file was generated by a fishnet (XTools) function run in ArcGIS 10.6.1 and the index IDs were transferred from the previous file through a spatial join. The origin of these block definitions can be found in CDFG Fish Bulletin 44 - The Commercial Fish Catch of California for the Years 1930-1934, Inclusive and CDFG Fish Bulletin 86 - The Commercial Fish Catch of California for the Year 1950 with A Description of Methods Used in Collecting and Compiling the Statistics. Some revision of block boundaries and indices occurred in the late 1990's or early 2000 (exact date unknown). Improvements over the previous version include correction to a small shift in cell positions, adjustments to blocks around the Mexican border and addition the large offshore 4-digit blocks previously managed in a separate file. Attributes: Block_ID: Unique identifier for commercial fishing block. locationDescription: Generalized description of block location.

A map service on the WWW for an ash survey on the Shaker State Forest in Canterbury, NH.

(Link to Metadata) Generated from exact latitude-longitude coordinates and projected from Geographic coordinates (Lat/Long) NAD83 into State Plane Meters NAD83. The Arc/Info GENERATE command was used with the following parameters; generate BoundaryTile_QUAD83 fishnet no labels -73.500,42.625 -73.500,42.725 0.125,0.125 20,17 The Arc/Info project command was then used to re-project from Geographic (DD)NAD83 into Vermont State Plane Meters (NAD83). Extraneous polygons where removed. Polygon label points where transfered from the QUAD coverage into the new coverage, resulting in duplicate attribute items. The tics in this data layer should only be used for digitizing if your source data is in NAD83! Use BoundaryTile_QUAD27 if your source data is in NAD27. In both cases you should re-project this coverage into UTM before digitizing. When you've completed your digitizing work re-project the data back into Vermont State Plane Meters NAD83.

Coastal grid of 6-acre cells containing 2010 and 2019 canopy estimates, change values, and site visit observations for incorporated areas in Chatham, Glynn, Bryan, Liberty, McIntosh, and Camden Counties, Georgia. Grid created using the Fishnet tool in ArcGIS Pro. 2010 and 2019 canopy data was aggregated to each grid cell to determine change. Cells with significant change +- 3 acres of loss or gain were visually inspected to determine areas for site visits.

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high motorcycle (Motorcycle: Above 125cc, Motorcycle: 125cc and under, Quadru-cycle, Motor Tricycle) crash counts that resulted in injuries (slight, serious, fatalities) observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018 and 2019. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 157 KBNumber of Files: The dataset contains a total of 158 road intersection records (11 "high-high" clusters and 147 "high-low" outliers)Date Created: 22nd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved either a motor tricycle, motorcycle above 125cc, motorcycle below 125cc and quadru-cycles and that were additionally associated with a slight, severe or fatal injury type were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which these motorcycle crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with motorcycle crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2019

The Healthy Place Index (HPI) is a statewide Total Percentile Ranking (0 for least healthy - 100 for most healthy) aggregated at the census tract level, based on 8 domain scores, 25 individual indicators, and race or ethnicity percentages in each census tract.Fishnet & Report Processing Methods: The fishnet summarizes the healthy places index of each grid cell by extracting the percentile value that intersects with the fishnet cell centroid. To calculate the report measure, minimum, maximum, and mean index value were calculated for an area of interest.

This depository contains two data sets:1. Collected and analysed field data related to herbivore browsing, and2. The 50 x 50 km fishnet (GIS data) as applied in:Per Angelstam P., Manton M., Pedersen S. and M. Elbakidze 2017. Disrupted trophic interactions affect recruitment of boreal deciduous and coniferous trees in northern Europe. Ecological Applications xxPlease note, other data used in this publication can be sourced from the original data sources (see cited literature for more information).

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters.The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. Identify local maxima candidates.Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for InfoUSAdata, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The next step was to qualitatively comparing results at each scale to create the final map of 72 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas. Finally, in order to serve land use and travel demand modeling purposes for Connect SoCal, job centers were joined to their nearest TAZ boundaries. While the identification mechanism described above uses a combination of point and grid cell boundaries, the job centers boundaries expressed in this layer, and used for Connect SoCal purposes, are built from TAZ geographies. In Connect SoCal, job centers are associated with one of three strategies: focused growth, coworking space, or parking/AVR.Data Field/Value description:name: Name of job center based on name of local jurisdiction(s) or other discernable feature.Focused_Gr: Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 1: center was used, 0: center was not used.Cowork: Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 1: center was used, 0: center was not used.Park_AVR: Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 1: center was used, 0: center was not used. nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.emp16: Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres: Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100.

The South Coast Missing Linkages project addresses fragmentation at a landscape scale. The approach is to identify and prioritize linkages that conserve essential biological and ecological processes by gathering current biological data for each linkage design to ensure the viability of the full complement of species native to the region.Fishnet & Report Processing Methods: The fishnet summarizes acres of linkages within each grid cell. To calculate the report measure, total acres of linkages were summed for an area of interest.

This dataset provides comprehensive information on road intersection crashes recognised as "high-low" outliers within the City of Cape Town. It includes detailed records of all intersection crashes and their corresponding crash attribute combinations, which were prevalent in at least 5% of the total "high-low" outlier road intersection crashes for the years 2017, 2018, 2019, and 2021. The dataset is meticulously organised according to support metric values, ranging from 0,05 to 0,0278, with entries presented in descending order.Data SpecificsData Type: Geospatial-temporal categorical dataFile Format: Excel document (.xlsx)Size: 0,99 MBNumber of Files: The dataset contains a total of 10212 association rulesDate Created: 23rd May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, PythonProcessing Steps: Following the spatio-temporal analyses and the derivation of "high-low" outlier fishnet grid cells from a cluster and outlier analysis, all the road intersection crashes that occurred within the "high-low" outlier fishnet grid cells were extracted to be processed by association analysis. The association analysis of the "high-low" outlier road intersection crashes was processed using Python software and involved the use of a 0,05 support metric value. Consequently, commonly occurring crash attributes among at least 5% of the "high-low" outlier road intersection crashes were extracted for inclusion in this dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

Proposed and existing bike routes within the SCAG region, compiled from local jurisdictions. Preprocessing methods: Dissolved all bikeways into one feature. Fishnet & Report Processing Methods: The fishnet summarizes miles of bikeways within each grid cell. To calculate the report measure, total miles of bikeways were summed for an area of interest.

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high public transport (Bus, Bus-train, Combi/minibus, Midibus) crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018, 2019, and 2021. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 49,0 KBNumber of Files: The dataset contains a total of 40 road intersection records (28 "high-high" clusters and 12 "high-low" outliers)Date Created: 21st May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved either a bus, a bus/train, combi/minibus and midibuses were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which the public transport crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections with public transport crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

The Toxics Release Inventory (TRI) tracks the management of toxic chemicals that pose a threat to human health and the environment.Fishnet & Report Processing: The fishnet summarizes the number of facilities within each grid cell. To calculate the report measure, total number of facilities were summed for an area of interest.

This dataset contains the boundaries for of federal Indian reservations in the six counties in the Southern California Association of Governments (SCAG) region, as defined by the United States Census Bureau.Fishnet & Report Processing Methods: Not in fishnet (descriptive measure entered into report separate from fishnet). The report measure lists whether a Native American reservation is present in an area of interest.

Hiking trails throughout the SCAG region, including trails in National Parks, California state parks, U.S. Forest Service lands, and within Los Angeles, Orange, Riverside, and Ventura Counties. There may be additional hiking trails not mapped in the greenprint. Preprocessing methods: Merged trails data from seven sources into one dataset. Dissolved all trails into one feature. Fishnet & Report Processing Methods: The fishnet summarizes miles of trails within each grid cell. To calculate the report measure, total miles of trails were summed for an area of interest. "https://services5.arcgis.com/ZldHa25efPFpMmfB/arcgis/rest/services/M_PCT_HalfmileProject_Centerline/FeatureServer", "https://nps.maps.arcgis.com/home/item.html?id=7b92e04dc7c74f269ba620e7540f9dbb", "https://nps.maps.arcgis.com/home/item.html?id=a4205715e04343638cfbc74ef128482d", "https://data.fs.usda.gov/geodata/edw/datasets.php", "https://www.parks.ca.gov/?page_id=29682", "https://egis-lacounty.hub.arcgis.com/datasets/lacounty::countywide-multi-use-trails-hosted-public/about", "https://data-ocpw.opendata.arcgis.com/datasets/a75cdbabf08e41e49d14aa4479e1061a_0", "https://venturacountyactiveoutdoors-vcitsgis.hub.arcgis.com/datasets/vcitsgis::county-trails/about", "https://catalog.data.gov/dataset/tiger-line-shapefile-2018-county-riverside-county-ca-all-roads-county-based-shapefile"

The objective of this project was to digitally map the boundaries of Audubon California's Important Bird Areas (IBA). Existing Important Bird Areas identify critical terrestrial and inland water habitats for avifauna, in particular, habitat that supports rare, threatened or endangered birds and/or exceptionally large congregations of shorebirds and/or waterfowl. The digitization of Important Bird Areas represents an important first step in conservation planning of these critical habitats using GIS. For more information, visit: https://docs.audubon.org/sites/default/files/documents/auduboncalifornia_gtr_iba_200812.pdfImportant Bird Areas (IBAs) identify critical terrestrial and inland water habitats for avifauna; in particular, habitat that supports rare, threatened or endangered birds and/or exceptionally large congregations of shorebirds and/or waterfowl. Overall bird conservation efforts throughout California by Audubon California. Fishnet & Report Processing Methods: The fishnet summarizes acres of important bird areas within each grid cell. To calculate the report measure, total acres of important bird areas were summed for an area of interest.