Field descriptions for the James City County Parcel layer and the Data table.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Soccer Field Point dataset.

This guide will teach you everything you need to know to successfully migrate your field workflows to Field Maps.

In this seminar, the presenters will introduce essential concepts of Collector for ArcGIS and show how this app integrates with other components of the ArcGIS platform to provide a seamless data management workflow. You will also learn how anyone in your organization can easily capture and update data in the field, right from their smartphone or tablet.This seminar was developed to support the following:ArcGIS Desktop 10.2.2 (Basic)ArcGIS OnlineCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

A map of the University of Mississippi Field Station and accompanying GIS data

Seattle Parks and Recreation GIS Map Layer Shapefile - Lacrosse Field

Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata.

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Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/26

Data record audit fields in feature classes within the MD DoIT iMap spatial database.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Rugby Field dataset.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Track Field dataset.

Essential tasks and best practices for taking your web maps offline and into the field with ArcGIS Field Maps.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Trails dataset.

The Community Map (World Edition) web map provides a customized world basemap that is uniquely symbolized and optimized to display special areas of interest (AOIs) that have been created and edited by Community Maps contributors. These special areas of interest include landscaping features such as grass, trees, and sports amenities like tennis courts, football and baseball field lines, and more. This basemap, included in the ArcGIS Living Atlas of the World, uses the Community vector tile layer. The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the layer items referenced in this map.

There are many useful strategies for preparing GIS data for Next Generation 9-1-1. One step of preparation is making sure that all of the required fields exist (and sometimes populated) before loading into the system. While some localities add needed fields to their local data, others use an extract, transform, and load process to transform their local data into a Next Generation 9-1-1 GIS data model, and still others may do a combination of both.There are several strategies and considerations when loading data into a Next Generation 9-1-1 GIS data model. The best place to start is using a GIS data model schema template, or an empty file with the needed data layout to which you can append your data. Here are some resources to help you out. 1) The National Emergency Number Association (NENA) has a GIS template available on the Next Generation 9-1-1 GIS Data Model Page.2) The NENA GIS Data Model template uses a WGS84 coordinate system and pre-builds many domains. The slides from the Virginia NG9-1-1 User Group meeting in May 2021 explain these elements and offer some tips and suggestions for working with them. There are also some tips on using field calculator. Click the "open" button at the top right of this screen or here to view this information.3) VGIN adapted the NENA GIS Data Model into versions for Virginia State Plane North and Virginia State Plane South, as Virginia recommends uploading in your local coordinates and having the upload tools consistently transform your data to the WGS84 (4326) parameters required by the Next Generation 9-1-1 system. These customized versions only include the Site Structure Address Point and Street Centerlines feature classes. Address Point domains are set for address number, state, and country. Street Centerline domains are set for address ranges, parity, one way, state, and country. 4) A sample extract, transform, and load (ETL) for NG9-1-1 Upload script is available here.Additional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.

Seattle Parks and Recreation GIS Map Layer Shapefile - Football Field Point

Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata.

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Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/17

Overview

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

Preprocessing/Formatting

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

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

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

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

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

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

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

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

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

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

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

Known Uses

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

Further Documentation and Resources

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

GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

This layer (hosted feature layer) depicts the parks' green spaces and sports fields in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division.For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

The delineation of agricultural field boundaries has a wide range of applications, such as for crop management, precision agriculture, land use planning and crop insurance, etc. Manually digitizing agricultural fields from imagery is labor-intensive and time-consuming. This deep learning model automates the process of extracting agricultural field boundaries from satellite imagery, thereby significantly reducing the time and effort required. Its ability to adapt to varying crop types, geographical regions, and imaging conditions makes it suitable for large-scale operations.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-2 L2A 12-bands multispectral imagery using Bottom of Atmosphere (BOA) reflectance product in the form of a raster, mosaic or image service.OutputFeature class containing delineated agricultural fields.Applicable geographiesThe model is expected to work well in agricultural regions of USA.Model architectureThis model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.64 for fields.Training dataThis model has been trained on an Esri proprietary agricultural field delineation dataset.LimitationsThis model works well only in areas having farmlands and may not give satisfactory results in areas near water bodies and hilly regions. The results of this pretrained model cannot be guaranteed against any other variation of the Sentinel-2 data.Sample resultsHere are a few results from the model.

AT_2004_HARF File Geodatabase Feature Class Thumbnail Not Available Tags Socio-economic resources, Information, Social Institutions, Hierarchy, Territory, BES, Parcel, Property, Property View, A&T, Database, Assessors, Taxation Summary Serves as a basis for performing various analyses based on parcel data. Description Assessments & Taxation (A&T) Database from MD Property View 2004 for Harford County. The A&T Database contains parcel data from the State Department of Assessments and Taxation; it incorporates parcel ownership and address information, parcel valuation information and basic information about the land and structure(s) associated with a given parcel. These data form the basis for the 2004 Database, which also includes selected Computer Assisted Mass Appraisal (CAMA) characteristics, text descriptions to make parcel code field data more readily accessible and logical True/False fields which identify parcels with certain characteristics. Documentation for A&T, including a thorough definition for all attributes is enclosed. Complete Property View documentation can be found at http://www.mdp.state.md.us/data/index.htm under the "Technical Background" tab. It should be noted that the A&T Database consists of points and not parcel boundaries. For those areas where parcel polygon data exists the A&T Database can be joined using the ACCTID or a concatenation of the BLOCK and LOT fields, whichever is appropriate. (Spaces may have to be excluded when concatenating the BLOCK and LOT fields).

The Vegetation Technical Working Group (VTWG) of the Alaska Geospatial Council developed the Minimum Standards for Field Observation of Vegetation and Related Properties Version 1.1 (August 2022) to help ensure that vegetation data collected as part of independent vegetation survey, mapping, monitoring, and classification projects can support the production of a statewide vegetation map from quantitative data and the continued development of the U.S. National Vegetation Classification.