The GIS shapefile and summary tables provide irrigated agricultural land-use for Citrus, Hernando, Pasco, and Sumter Counties, Florida through a cooperative project between the U.S Geological Survey (USGS) and the Florida Department of Agriculture and Consumer Services (FDACS), Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated land field verified for 2019, crop type, irrigation system type, and primary water source used in Citrus, Hernando, Pasco, and Sumter Counties, Florida. A map image of the shapefile is provided in the attachment.

County wide mapping application

The City of New Port Richey's Open Data Hub allows city residents, landowners, business owners and land developers to explore, visualize and analyze the city through its GIS data.

Geospatial data about Pasco County, Florida Parcels. Export to CAD, GIS, PDF, CSV and access via API.

REQUIRED: A brief narrative summary of the data set.

This data set consists of a detailed digital map of the extent of fields and a summary of the irrigated acreage for the period between January and December 2019 compiled for Citrus, Hernando, Pasco, and Sumter Counties, Florida. Attributes for each field include a general or specific crop type, irrigation system, and primary water source.

Contour data may be provided (not with all LiDAR data sets). This dataset is provided for visualization only and is not to be used for Digital Terrain Model construction

Planning Areas: CC_SPA_Zones

Pasco County cities

This Map service is the replacement for the original PascoBase Map Service. It has been changed to 27 source data, WebMercator Projection, and ArcPro runtime. It should be used to replace the original PascoView/PascoBase. The service contents have not been changed at all.

The Geographic Information Systems (GIS) market is experiencing robust growth, projected to reach $2979.7 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033. This expansion is driven by several factors. Increasing urbanization necessitates efficient city planning and management, fueling demand for advanced GIS solutions. Furthermore, the rise of smart cities initiatives, coupled with the growing adoption of cloud-based GIS platforms, provides scalability and cost-effectiveness, accelerating market penetration. Precision agriculture, requiring detailed land analysis for optimized resource allocation, represents another significant driver. The integration of GIS with other technologies, such as IoT and AI, enhances data analysis capabilities, further boosting market value. Competition among key players like Pasco, Ubisense Group, and Hexagon is fostering innovation and creating diverse product offerings tailored to specific industry needs. While data security and integration challenges pose potential restraints, the overall market outlook remains positive, driven by continuous technological advancements and expanding applications across various sectors. The market's segmentation, while not explicitly detailed, likely includes various software and hardware components, professional services, and implementation support. The regional distribution is expected to see significant growth in developing economies, particularly in Asia-Pacific, driven by rapid urbanization and infrastructure development projects. North America and Europe will likely continue to dominate the market due to established technological infrastructure and higher adoption rates. However, the widening digital divide and increased investment in emerging economies are likely to shift regional market shares over the forecast period. The historical period (2019-2024) likely witnessed a slightly lower CAGR than the projected 5.5%, given the initial stages of widespread cloud adoption and the maturing of several key technologies. The next decade will see increased consolidation within the market, with larger players acquiring smaller companies to expand their product portfolios and geographical reach.

As-built Upload FormThis form is for submitting electronic asbuilt data to the GIS Team. Please upload all required files.

These data were automated to provide an accurate high-resolution historical shoreline of Columbia River, Pasco to Richland, Washington suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or fi...

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

Shaded relief data created in 2019 using 2018 terrain data for Pasco, WA.

The Pasco runways feature class was digitized using 2020 aerial imagery and with information provided from the following sources: FDOT Aviation office, https://www.airnav.com/airport/X39 & Pasco County Attorney Office.

Subdivision polygon feature class holds the subdivision boundaries for the Pasco County tax maps.

Outstanding Florida Waters, (OFW), are waters designated worthy of special protection because of their natural attributes. This special designation is applied to certain waters, and is intended to protect and maintain existing acceptable quality standards. The OFW layer is a GIS spatial dataset that represents the OFW boundaries throughout the state of Florida. This project involves adding new data to and modifying existing data within the OFW data layer for better accuracy and representation. Boundaries for Outstanding Florida Waters (OFWs) as described in Section 62-302.700, F.A.C. This layer includes all three types of OFWs: OFW Aquatic Preserves, Special OFWs, and Other OFWs.

Contour data may be provided (not with all LiDAR data sets). This dataset is provided for visualization only and is not to be used for Digital Terrain Model construction

The first draft of the shifted parcel layer using Dx Dy coefficients from the hand adjusted parcel layer. The DxDy coefficients were collected and mass loaded into the Spatial Adjustment Adjust Tool in groups of no larger than 43,000 control points per adjustment and then adjusted using the rubbersheet technique. The adjusted sections were then assembled into one cohesive parcel map for Pasco County, Florida