Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS
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Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.
Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.
Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area.
Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.
Methods
Data acquisition and description
The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report.
Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm).
With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037.
Preparation and Creation of Model Factor Parameters
Creation of Elevation Factor
All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively.
Creation of Slope Factor
A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively.
Creation of Curvature Factor
Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor
As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively.
Creation of Human Population Distribution Factor
Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively.
Creation of Proximity to Health Facilities Factor
The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively.
Creation of Proximity to Road Network Factor
The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the
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TransportationThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, displays primary roads, secondary roads, local roads and railroads in the United States. According to the USCB, "This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways."Interstates 20 and 635Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (TIGERweb/Transportation) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 155 (Series Information for All Roads County-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Transportation - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Census Feature Class Codes (CFCC)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets
This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste. The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions.
Washes displays the natural drainage of the area. This data theme contains multiple regulatory classifications, the correct current classification is stored in the CFS_CODE2, CFS_NUM2, and CFS_SHORT2 fields. Lineage: Future plans to merge this layer with layer Wash_02K and the City's layer wash_ci, then rectify. Spatial Domain: Pima County Rectified: orthophoto90 Maintenance Description: The washes feature class is exported from the geodatabase on a nightly basis. The washes annotation is in a separate cover: washanno Naming unknown/unnamed washes from USGS quad....04/14/08 Maintenance Format: GDB Std Export Primary Source Organization: PC Primary Source Document: Orthophotos Primary Source Date: 1990 Primary Source Scale: 12000 Secondary Source Organization: USGS Secondary Source Document: Transportation DLG Secondary Source Date: 1988 Secondary Source Scale: 100000 GIS Contact: Mary Beth Clark MapGuide Layer Name: Washes - All MapGuide Scale Range: 0 - 100000PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsKnown Errors/Qualifications: Orthophoto washes are more detailed and are not edgematched to DLG washes. In the current regulatory scheme all washes between 2,000 CFS and 10,000 CFS are stored in the 5,000 to 10,000 category pending review.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976
Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The
Data Layer Name: Vermont Rational Service Areas (RSAs)
Alternate Name: Vermont RSAs
Overview:
Rational Service Areas (RSAs), originally developed in 2001 and revised in 2011, are generalized catchment areas relating to the delivery of primary health care services. In Vermont, RSA area delineations rely primarily on utilization data. The methods used are similar to those used by David Goodman to define primary care service areas based on Medicare data, but include additional sources of utilization data. Using these methods, towns were assigned based on where residents are going for their primary care.
The process used to delineate Vermont RSAs was iterative. It began by examining utilization patterns based on: (1) the primary care service areas that Goodman had defined for Vermont from Medicare data; (2) Vermont Medicaid assignments of clients to primary care providers; and, (3) responses to the “town of residence”/”town of primary care” questions in the Vermont Behavioral Risk Factor survey. Taking into account the limitations of each of these sources of data, VDH statisticians defined preliminary town centers and were able to assign approximately two/thirds of the towns to a town center. For towns with no clear utilization patterns, they examined mileage from these preliminary centers, and mileage from towns that had primary care physicians. Contiguity of areas was also examined. A few centers were added and others were deleted. After all towns were assigned to a center and mapped, outliers were identified and reviewed by referring to both mileage maps and utilization patterns. Drive time information was not available. In some cases where the mileage map seemed to indicate one center, but the utilization patterns were strongly supportive of another center, utilization was used as a proxy for drive time.
Preliminary RSAs were presented to the Vermont Primary Care Collaborative, the Vermont Coalition of Clinics for the Uninsured and other community members for their feedback. Department of Health District Directors from the Division of Community Public Health were also consulted. These groups suggested modifications to the areas based on their experience working in the areas in question. As a result of this review a few centers were added, deleted and combined, and several towns were reassigned. The Vermont Primary Care Collaborative reviewed the final version of RSAs.
The result of this process is 38 Rational Service Areas.
Given the limitations of the information available for this purpose, the delineation approach was deemed reasonable and has resulted in a set of RSAs that have been widely reviewed and accepted. Because of the iterative process, it is recognized that this is not a "pure" methodology in the sense that someone else attempting to replicate this process would probably not produce exactly the same results.
RSAs have been reviewed periodically to keep up with changes in demographics and provider practice locations. One revision occurred in 2011. This 2011 revision took towns that had originally been assigned as using out-of-state providers and reassigned them to Vermont RSAs.
Technical Details:
Vermont RSAs were defined using 3 sources of primary care utilization data and mileage maps. Each of the data sources had limitations, and these limitations had to be considered as towns were assigned to a RSA. A description of each of these data sources is provided.
Medicare utilization data was obtained from the Primary Care Service Areas developed by David Goodman using 1996 and 1997 Medicare Part B and Outpatient files. Thirty-eight primary care service areas were defined for Vermont. The major limitation of these assignments was that they were based on zip codes rather than town boundaries. Many small towns do not have their own zip code, or the town may be divided into multiple zip codes shared with multiple other towns. As the utilization data was reviewed consideration was given to whether the zip code in question represented the town, or whether utilization from that town may have been masked by a larger town's utilization patterns. A second consideration was that the Medicare data used 1996 & 1997 utilization. In areas where there were new practices established after 1997, the Medicare data would not be able to reflect their utilization.
Medicaid claims data only included children age 17 and under. The file contained Medicaid clients in 2000 with the town of residence of the client and the town of the primary care provider. The limitation in this file was that although the Medicaid database included a field for the geographic location of the provider separate from the mailing address, after examining the file it was determined that in many cases the mailing address was also being entered into the geographic location. In areas where practices were owned by a larger organization, the utilization patterns could not be determined. For example, in the St. Johnsbury RSA there were practices owned by an out-of-state medical center. Although it is known that there are medicaid providers in some of the towns in that area, all of the utilization was coded to out of state. Therefore the Medicaid data had to be disregarded in this area. The St. Johnsbury RSA was subsequently defined around three town centers (St. Johnsbury, Lyndon, and Danville) because more precise utilization patterns could not be distinguished.
The BRFSS data was obtained from the 1998-2000 surveys. Respondents were asked for the town of their primary care provider. The town of residence of the respondent is also collected. These responses represented all Vermonters age 18-64 years old, regardless of type of insurance. The limitation of this data was small number of respondents in the smaller towns.
Mileage information was obtained from the Vermont Medicaid program. This mileage information was derived using GIS mapping software to assess all statewide roads. However, drive-time data could not be determined at that time because there was no distinction between primary and secondary roads. The Medicaid program applied GIS mapping software to assign clients to primary care providers using 15 miles as a proxy for 30-minute drive time. This standard was also used in 2001 when the original RSAs were developed.
The VDH Public Health Statistics program periodically updates RSA GIS data. (last updated in 2011)
The Education Facility point feature class represents public and private primary, secondary, and post-secondary school facilities in the Des Moines, Iowa metropolitan area. This dataset was created for a FEMA grant project (2008 Legislative Pre-Disaster Mitigation Grant Program, LPDM-2008-IA-77-002) to develop data and a methodology for performing GIS-based Disaster Mitigation Act of 2000 (DMA2000) Multi Hazard Mitigation Plan Risk Assessments. The data were developed from local, state, and national sources and prepared by the University of Northern Iowa GeoInformatics Training, Research, Education and Extension Center (GeoTree ) between May 2010 and April 2011. The data were accepted and are maintained by the City of Des Moines and Des Moines Area Regional GIS partners. This dataset was developed for use in the Des Moines Area Regional GIS, which supports public safety response and emergency management in the Des Moines metropolitan area and surrounding counties. The Des Moines Area Regional GIS is a shared data repository hosted by the City of Des Moines. The Regional GIS partnership includes City of Des Moines, Polk County, and several cities within Polk County. Each partner is responsible for maintaining data for their respective jurisdiction.
[Metadata] Wetlands in the State of Hawaii. Source: USFWS, November 2024. (https://www.fws.gov/program/national-wetlands-inventory/data-download). This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the State of Hawaii.
These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class (downloadable from the USFWS website via the link shown above). Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer (see link above), which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.
For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/wetlands.pdf or complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/wetlands.html or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.
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Data Description:
This dataset presents the spatial outcome of an analysis modelling perceived green space quality across the city of Espoo, Finland. The analysis relies on data gathered through the My Espoo on the Map survey (Mun Espoo kartalla) in the autumn of 2020 as part of the NordForsk-funded research project NORDGREEN. A comprehensive account of the analytical process and potential applications of the dataset is available in the associated publication, "Predicting context-sensitive urban green space quality to support urban green infrastructure planning" (open access: https://doi.org/10.1016/j.landurbplan.2023.104952).
Data Processing:
This dataset results from an analysis that integrates both primary and secondary sources of geospatial data. The primary data were collected with an online public participation GIS (PPGIS) survey directed for the adult inhabitants of Espoo. The data collection took place in September-October 2020 and was executed in collaboration with Aalto University and the City of Espoo. For a detailed overview of the data collection process, please refer to the related publication.
Data characteristics:
Format: Shapefile (50m x 50m grid)
Geographical area: Espoo, Finland
Spatial reference: EUREF FIN TM35FIN
Note: Only grid cells intersecting with greenspace have been included in the dataset. For the employed definition of green areas, please consult the related publication.
Data attributes and their descriptions:
"P_PROB": Probability (P), positive perceived quality
"N_PROB": Probability (P), negative perceived quality
Funding:
This research was funded by NordForsk, Sustainable Urban Development and Smart Cities Programme, Project Smart Planning for Healthy and Green and Nordic Cities – NORDGREEN, under Grant Number: 95322.
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.
Two foot interval contour lines created by CMG Drainage as part of the 2014 Green Valley Basin Management Study. Ground surface was derived from 2008 and 2011 PAG LiDAR. Known Errors/Qualifications: Topo elevations are +- 1 foot accuracy. Spatial Domain: Eastern Pima County Maintenance Format: Shape Primary Source Organization: CMG Drainage Primary Source Date: 20141101 Primary Source Format: DWG Secondary Source Organization: RFCD Secondary Source Contact: Kenneth Maits Secondary Source Format: Shape GIS Contact: Kenneth Maits, Two foot interval contour lines created by CMG Drainage as part of the 2014 Green Valley Basin Management Study. Ground surface was derived from 2008 and 2011 PAG LiDAR. Known Errors/Qualifications: Topo elevations are +- 1 foot accuracy. Spatial Domain: Eastern Pima County Library Input: Shape Std Primary Source Organization: CMG Drainage Primary Source Date: 20141101 Primary Source Format: DWG Secondary Source Organization: RFCD Secondary Source Contact: Kenneth Maits Secondary Source Format: Shape GIS Contact: Kenneth Maits
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.
firestat displays fire stations. Lineage: Corrected address for San Xavier District fire station - 4/25/2007. Added Three Points Fire District station 92 - 4/25/2007. Added Northwest Fire District’s new station 38 - 7/12/2007. Changed to Golder Ranch Fire District station 116 from Rural Metro Fire Department station 77 - 7/12/2007. Added GVFD 154 -8/21/2007. Moved GRFD 110, NWFD 30 and 34, RFD72 and 73, TFD 3,4,5,17,and 93 to moved to the correct location - 8/21/2007. Corrected address for NWFD 32 - 8/21/2007. Added Arivaca Volunteer Fire Department - 2/11/2008 Added Why Fire Department - 4/30/2008 The station numbers for NWFD, GRFD, and Three Points were reassinged - 06/28/08 New GRFD 376 replaced the old GRFD 116 located on 700 E Palisades Dr - 06/28/08 Added new station 182 to Colona De Tucson and also added new station 2 in Rincon Valley - 02/02/09 NWFD station 333 was relocated to 2824 W Ina RD from 3701 W Quasar ST -03/25/09 Moved Tucson Fire Sta.@797 E. Ajo Way to new location @ 300 S. Fire Central Pl - 09/30/09 NWFD station 339 – 12095 N Thornydale Rd, Marana GRFD Station 377 – 355 E Linda Vista, Oro Valley GRFD Station 378 – 60891 Arroyo Vista Dr Picture Rocks Station 120 – 7341 N Sandario Rd 03/24/2010 Corrected STNO for 401,402,403,404, and added 405. Moved STNO 151 to 250 N La Canada Drive from accross the street. Added STNO 155 which opens July. Correction on station no from 120 to 121. 3/29/2010 Added rual/metro station no. 81 and updated the address for staion no. 79. Added NW st no 349@ 3701 W Quasar St. 07/19/2010 Moved TON Firestation to 2070 W San Xavier RD 10/22/2010 Added MTVista Station 620, 9310 N. Shannon 01/26/2011 Added Avra Vally Sta 192 10/27/2011 Spatial Domain: Pima County Rectified: parcel Maintenance Organization: PC ITD GIS Maintenance Description: Updates are notified to GIS departmet by Fire Districts, or 911 Communications Department. Maintenance Frequency: Annually Maintenance Format: Shape Primary Source Organization: Fire Districts Primary Source Date: 1994 Primary Source Scale: 3000 Secondary Source Organization: Northwest Fire District/Tucson Fire Secondary Source Contact: Mike Duncan/Jim Long/Ann Moser Secondary Source Date: 20050309 Secondary Source Format: Shape GIS Contact: Kaoru Johansen
MapGuide Layer Name: Fire Stations MapGuide Scale Range: 0 - 200000
These data were collected to support a drought-vulnerability assessment and near real-time drought awareness web tool for public water systems (PWS) on surface water supply in West Virginia. PWS withdrawal rates were evaluated against USGS low-flow stream statistics, modeled streamflow from the National Water Model, and thresholds from state drought response guidelines and ecological-flow literature. Other PWS information relevant to water management, including flow regulation and water storage is included. Description of Data: These data are available in Excel (.xlsx) files and comma-separated text files (.csv) for access in nonproprietary formats. The "sites" file contains attribute information for each PWS intake, including flow regulation and reservoirs. The "wd" file contains the monthly withdrawal information used to generate summary statistics. Data Sources: These data were not collected by the USGS. Monthly water withdrawal data for public water systems (PWS) was provided by West Virginia Department of Environmental Protection's Large Quantity User (LQU) reporting program. These data were used to calculate monthly withdrawal rates for selected PWS using surface water supply. The LQU dataset is self-reported. Basic quality control checks, including summary statistics, box plots, and time series plots were performed and data-entry errors were corrected when identified. PWS with redundant intakes on the same waterbody (primary and secondary) had withdrawals from the secondary intake (ID007, ID073, ID084, ID098, and ID101) reassigned to the primary intake and the secondary intake was removed from further analysis. Streamflow regulation and minimum flows were determined by a GIS tool developed by the Technical Applications and GIS Unit of the West Virginia Department of Environmental Protection (WVDEP). The presence and storage capacity of reservoirs was determined by review of information from the U.S. Army Corps of Engineers' National Inventory of Dams. The presence of smaller dams and weirs was determined by aerial or satellite imagery and noted, but no further effort was made to estimate storage capacity or impact on streamflow. Note: Disclosing specific location information for PWS intakes conflicts with West Virginia state law and USGS policy. For this publicly-accessible USGS Data Release and the near real-time drought awareness web tool, PWS locations are aggregated to the county or 10-digit hydrologic unit code (HUC10) watershed. Further discussion of data, methods, analysis, and limitations are included in the associated USGS Open File Report 2023-XXXX.
This data set shows the centerlines of all public and some private roads within the state of Minnesota. Segments of pavement may have only one route or muitple routes traveling over them. One route will always be idenflied as primary and all attributes of these roadways signed to it. Other routes sharing the same pavement will be consisted co-incident or secendary and will not be a signed roadway attributes.
State highways are divided into segments called control sections for record keeping, maintenance, construction, and other administrative purposes. The four-digit control section number is composed of the two-number county code and an identifying two-digit number within that county. Control sections are revised due to jurisdictional transfers (typically from state to county) when new highway segments or entirely new state highways are built.
Routes State AID represent road centerlines for all state aid routes within the state of Minnesota.
Check other metadata records in this package for more information on routes centerlines.
Links to ESRI Feature Services:
Coincident Routes in Minnesota: Coincident Routes
MnDOT Control Sections: MnDOT Control Sections
MnDOT Roadway Routes in Minnesota: MnDOT Roadway Routes
Primary Routes in Minnesota: Primary Routes
State Aid Routes in Minnesota: State Aid Routes
Trunk Highways in Minnesota: Trunk Highways
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The Department of Defense and the other desert managers are developing and organizing scientific information needed to better manage the natural resources of the Mojave Desert. One product from this endeavor is the Central Mojave Vegetation Map (developed by US Dept of Interior, USGS Western Ecological Research Center and Southwest Biological Science Center) that displays vegetation and other land cover types in the eastern Mojave of California. Map labels represent alliances and groups of alliances as described by the U.S. National Vegetation Classification. The nominal minimum mapping unit is 5 hectares. Each map unit is labeled by a primary land cover type and a secondary type where applicable. In addition, the source of data for labeling each map unit is also identified in the attribute table for each map unit. Data were developed using field visits, 1:32,000 aerial photography, SPOT satellite imagery, and predictive modeling.
This datasets contain GIS shapefiles related to the transportation infrastructure of Accomack and Northampton Counties on the Eastern Shore of Virginia. Included here are roads and highways, railroads, airfields and airstrips, boat ramps, and electrical transmission lines. Data was compiled from multiple sources. The primary purpose of this dataset is to provide VCRLTER researchers and students with a convenient up-to-date set of GIS data layers in one location that can be used as base layers for various map products and for planning research activities. A secondary purpose of this dataset is to extend transportation data coverage in the VCRLTER data catalog to include Accomack County and to supersede older USGS DLG data contained in the Northampton County GIS data package (VCRLTER dataset VCR14219).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Any geological exploration of the Earth ultimately requires understanding its structure and crustal geometry and composition - its architecture - whether we are searching for battery minerals, metals, water, hydrocarbons, carbon storage reservoirs, or geothermal. Although regional and local databases are available, especially in the commercial world, there is no systematic, global suite of databases for crustal architecture and structure accessible by the entire scientific community. This is why we have built Reclus. The Reclus suite includes databases of the following: (1) structural elements, which define the three-dimensional geometry of the rock volume, including folds and faults; (2) 'crustal' facies describing the geometry and composition/rheology of the lithosphere; (3) igneous features; and (4) geodynamics, representing the dominant thermo-mechanical processes acting on the lithosphere. The datasets provided here are for East Africa and are described in detail in Markwick et al., (Accepted for publication). Interpretations were made between 2017-2021 using a range of primary and secondary sources. These input datasets include gravity and magnetic data, Landsat imagery, radar data, published well and seismic information, geological maps and published papers, MSc and Ph.d. theses, and reports. The databases are compiled and managed using ESRI's ArcGIS software and are underpinned by a comprehensive data management system and systematic attribution. In this resource, the databases are provided as ESRI shapefiles. Shapefiles are the ESRI data format that can be used most widely, including the following: different versions of ArcGIS; QGIS, Schlumberger's Petrel; and Google Earth. Reclus enables commercial explorationists to place their internal data and expertise within a systematically built, regional context. For students and academics, Reclus is designed to provide a starting point for further research - it is so much easier to take an existing resource, question it, disagree with it, change it, and improve it. Reclus is named after the French geographer Jacques Élisée Reclus”, who in the late 19th century compiled and analyzed physical and human geographic data for every continent. This was published in his 19 volume work, La Nouvelle Géographie Universelle, la Terre et Les Hommes, which included some of the first maps illustrating the global distribution of volcanoes and mountains.
Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS