This parcels dataset is a spatial representation of tax lots for Cape May County, New Jersey that have been extracted from the NJ statewide parcels composite by the NJ Office of Information Technology, Office of GIS (NJOGIS). Parcels at county boundaries have been modified to correspond with the NJ county boundaries and the parcels in adjacent counties.This GIS parcel data set was created by using scanned county tax maps. The scanned images were georeferenced to the 2002 color digitial orthophotos for the State of New Jersey. Software customization was developed to standardize data capture as well as processing. Quality control/quality assurance methods were developed and used throughout the entire data capture and processing. GIS processing was done using ESRI's ArcInfo and topology rules were applied to ensure proper connectivity between polygons. Each parcel contains a field named PAMS_PIN based on a concatenation of the county/municipality code, block number, lot number and qualification code. Using the PAMS_PIN, the dataset can be joined to the MOD-IV database table that contains supplementary attribute information regarding lot ownership and characteristics. Due to irregularities in the data development process, duplicate PAMS_PIN values exist in the parcel records. Users should avoid joining MOD-IV database table records to all parcel records with duplicate PAMS_PINs because of uncertainty regarding whether the MOD-IV records will join to the correct parcel records. There are also parcel records with unique PAMS_PIN values for which there are no corresponding records in the MOD-IV database tables. This is mostly due to the way data are organized in the MOD-IV database.The polygons delineated in the dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such.The MOD-IV system provides for uniform preparation, maintenance, presentation and storage of property tax information required by the Constitution of the State of New Jersey, New Jersey Statutes and rules promulgated by the Director of the Division of Taxation. MOD-IV maintains and updates all assessment records and produces all statutorily required tax lists for property tax bills. This list accounts for all parcels of real property as delineated and identified on each municipality's official tax map, as well as taxable values and descriptive data for each parcel. Tax List records were received as raw data from the Taxation Team of NJOIT which collected source information from municipal tax assessors and created the statewide table. This table was subsequently processed for ease of use with NJ tax parcel spatial data and split into an individual table for each county.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

This map depicts lands owned and/or administered by the U.S. Fish and Wildlife Service at Cape May National Wildlife Refuge.

description: This map depicts lands owned and/or administered by the U.S. Fish and Wildlife Service at Cape May National Wildlife Refuge.; abstract: This map depicts lands owned and/or administered by the U.S. Fish and Wildlife Service at Cape May National Wildlife Refuge.

These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways.

These are two land cover datasets derived from Landsat Thematic Mapper and Operational Land Imager (spatial resolution 30-m)Path 014 and Rows 032 and 033 surface reflectance data collected on July 14, 2011 and July 19, 2013, before and after Hurricane Sandy made landfall near Brigantine, New Jersey on October 29, 2012. The two land cover data sets provide a means of evaluating the effect of Hurricane Sandy of data sets collected at times that represent or approach peak vegetation growth. The most accurate results of the land cover classification are based on twelve classes, some of which occur adjacent to the marshes but not on the New Jersey intracoastal marshes. Twelve classes were used in the supervised maximum likelihood classification of the intracoastal marshes, three classes (forested wetlands, unconsolidated beach sediment and urban development areas) which occur only adjacent to the marshes, were masked out on the land cover maps. The twelve classes are based on the National Oceanic and Atmospheric Administration Coastal Change Analysis Program (C-CAP) and the New Jersey Department of Environmental Protection 2007 Land Use/Land Cover Data Set classes that could be identified on the Landsat TM surface reflectance bands 3-5 and Landsat OLI surface reflectance bands 4-6, and field work in 2014 and 2015. There is considerable confusion between classes due to the variation in the species and density of cover of vegetation, variation in the composition and density of the vegetation, variation in the composition and amount of the marsh substrate detected by the sensor, and the variation in tidal stage which strongly influences the surface reflectance of the pixel (Kearney et al. 2009). However, the identification of high marsh appears to be accurate based on field work validation. The high marsh contains one-to-three-meter-wide areas of low marsh that border the bays and lagoons and tidal creeks in the marshes, but that are too small to resolve with the Landsat sensors. Kearney, M.S., Stutzer, D.S., Turpie, K., and Stevenson, J.C. (2009) Spectral properties of marsh vegetation under inundation. Journal of Coastal Research 25: 1177-1186.

These data represent a digital form of the geologic map of Cape Cod and the islands. Note: These data were reprojected from their native projection into North American Datum 1983 (NAD83) / Massachusetts State Plane coordinate system, Mainland Zone (Fipszone 2001) meters by the Massachusetts Office of Coastal Zone Management on June 21, 2006.

This application summarizes information related to parcels with characteristics that may accommodate salt marsh migration. Data is organized into easy to use charts that are spatially linked to the on screen map. Click on a feature in a chart to see where the parcel is on the map. Application is intended for use by municipal open space committees and local land trusts in acquisition planning.Parcels with the future potential to accommodate salt marsh under a sea level rise scenario of 1 meter have been identified. Parcel selection and scoring was based on the following: 1. Parcel and suitable space contiguity 2. Ownership 3. Salt marsh adjacency 4. Total suitable area 5. Percentage of the parcel’s total area suitable for salt marsh migration In general parcels were scored relative to each other based on the percentage of the parcel’s total area suitable for salt marsh migration, a higher percentage resulted in a higher score. However, a few characteristics were considered to be highly desirable, resulting in the highest possible score regardless of relative percentage. All town, state, federal and conservation organization owned parcels were also removed, as this work primarily focuses on the identification of parcels for further review by municipal open space committees and local land trusts for future acquisitions planning. In total 229 parcels were identified: 93 in Eastham, 113 in Wellfleet, 23 in Truro and 0 in Provincetown. All suitable migration space in Provincetown was located within 3 parcels (federal and state owned), the majority in Cape Cod National Seashore. Please note the estimation of parcel area currently occupied by salt marsh was determined from the 'ISM Contemporary Salt Marsh Vegetation' layer. To locate possible parcels of interest a suitability base map was created to identify areas within the ISM planning area with the potential to accommodate salt marsh under a sea level rise scenario of 1 meter. The following criteria were considered: elevation, slope, connectivity and proximity to salt marsh and land cover.ElevationAreas with the future potential to accommodate salt marsh under a sea level rise scenario of 1 meter from current levels were identified and delineated based on an estimated suitable elevation range determined from the following generalized relationships between dominant salt marsh vegetation and tidal stage (Ayers, 1959; Redfield, 1972; Teal, 1986; Bertness, 1987; Bertness, 1991): Inland salt marsh boundary = Mean Higher High Water + 2.5 ft Seaward salt marsh boundary = Mean High Water – 2/3 MNThe current suitable elevation range for salt marsh within the ISM planning area was estimated to be -0.75 m (-2.46 ft) to 2.25 m (7.38 ft) NAVD88 (based on tidal profiles from Provincetown Harbor, Pamet Harbor, Wellfleet Harbor, Rock Harbor and Sesuit Harbor). To simulate 1 meter of sea level rise, both the upper and lower limits were adjusted by 1 meter. All areas with elevation values of 0.25 to 3.25 m were evaluated.SlopeSuitable slopes were determined based on Smith, 2020 and Kirwan et al., 2016, where the potential for marsh expansion generally decreases with increasing slope. Gentler slopes were most suitable (<1%), moderate slopes likely suitable (1-5%) and steeper slopes (>5%) less suitable. Severe slopes (>20%) were treated as unsuitable migration space. Connectivity and Proximity to Existing Salt MarshAreas were classified based on physical relationship and proximity to existing salt marsh and the presence of anthropogenic barriers (roads, parking lots, shoreline armoring, culverts) influencing salt marsh migration and then ranked accordingly. Areas with no hydrologic connection to existing salt marsh were treated as unsuitable.Land CoverWith no clear methodology for classifying land cover suitability for salt marsh migration (as demonstrated in Smith, 2020) general assumptions were made. The primary assumption reflects the concept that areas most suitable now (e.g., emergent wetlands) are more likely to be suitable in the future while the most uncertain transitions would be those dependent on forest retreat. Impervious area was classified as least suitable. Parcels The suitability base map was used to extract parcels intersecting the analysis area, and a series of operations were carried out to remove parcels selected due to noise in the data, parcels with minimal suitable space and parcels completely separated from other extracted parcels by topographic or anthropogenic barriers. Tax Parcel and assessor information was obtained from MassGIS Data: Property Tax Parcels (M086TaxPar last updated 4/2020, M242TaxPar last updated 6/2020, M300TaxPar last updated 11/2020, M318TaxPar last updated 2/2019). Please note a select number of parcels were designated as restoration parcels. These parcels currently contain large areas of mudflat and with increased deposition and/or human intervention could become more suitable in the future. Parcels designated as restoration parcels were not scored

The Geospatial Analytics Market size was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The growing adoption of location-based technologies and the increasing need for data-driven decision-making in various industries are key factors driving market growth. Geospatial analytics captures, produces and displays GIS (geographic information system)-maps and pictures that may be weather maps, GPS or satellite photos. The geospatial analysis as a tool works with state of art technology in every formats namely; the GPS, sensors that locates, social media, mobile devices, multi of the satellite imagery to produce data visualizations that are facilitating trend-finding in complex relations between people and places as well are the situations' understanding. Visualizations are depicted through the use of maps, graphs, figures, and cartograms that illustrate the entire historical picture as well as a current changing trend. This is why the forecast becomes more confident and the situation is anticipated better. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

The Upper Wetlands Boundary/Upper Wetlands Limit data is composed of two wetlands limit lines mapped in two separate NJDEP mapping programs. Those arcs identified as the Upper Wetlands Boundary (UWB) were delineated under the Wetlands Act of 1970 (N.J.S.A. 13:9A-1 et seq). The intent of this act was to regulate development in tidal wetlands of the state. The initial task outlined in the legislation was to identify and map where those tidal wetlands existed in the state. The tidal wetlands delineations were based on the presence of 25 common tidal marsh species, as well as the extent of tidally flowed bare ground. Areas delineated in the original program extend from Trenton on the Delaware River, south around the Cape May Peninsula, and then north to Perth Amboy on the Arthur Kill. UWB delineations under this program were officially promulgated, and the original UWB arcs form a legal regulatory boundary line. While tidally influenced areas do exist north of Perth Amboy, these areas were not mapped in this program due to funding constraints. In 1987, New Jersey passed the Freshwater Wetlands Protection Act (N.J.S.A. 13:9B-1). As part of the requirements of that act, the NJDEP was required to map all non-tidal wetlands of the state, as they existed on 1986 photo basemaps in a separate freshwater wetlands (FWW) mapping program. As tidal areas of the state were already under tidal wetlands regulations, they were to be excluded from the FWW regulations and from the FWW mapping program. Since the UWB, where it existed, was the regulatory boundary for the tidal wetlands program, it was incorporated into the FWW maps to identify the lower, or seaward, limit of the areas under FWW jurisdiction and mapping. All areas below the UWB were excluded from the FWW program; all areas above the UWB were to be mapped. Where the UWB had not been delineated, a functionally similar line was delineated from the 1986 products used in the FWW mapping to separate tidal from non-tidal areas. As with the UWB, areas below, or seawards, of this line were not mapped under the FWW program. However, since this new line was not delineated through the same procedures as the original UWB, and is not a promulgated regulatory line, it is not to be considered analogous to the UWB. To distinguish this new line from the original UWB, it has been given a new name, the Upper Wetlands Limit (UWL). The data layer also includes another type of coded line. To clarify the UWB delineation along the Atlantic coast barrier island area, the land/water interface as delineated in a 1986 land use/ land cover mapping project was also included. These arcs are identified as COASTLINE in the data set. These arcs do represent any delineations based on vegetation or other parameters associated with the UWB or UWL. Both of these lines were digitized as part of the FWW mapping program, and the UWB/UWL data layer has been extracted from the FWW maps, as described in the Process Steps.