Imagine you are sailing down a wide river and observing the changing landscape on either side. Fields give way to forests, tributaries and streams flow into the river, bridges cross over, and you know that one side of the river is managed by a government agency, while the other is subdivided into land ownership parcels of different sizes. The connectedness, adjacency, and proximity between these features can be summed up in one word: topology.Geodatabase topology allows you to define the spatial relationships you want protected in your GIS data. By doing so, no matter how much you edit, twist, bend, or squash your feature data, things stay connected, adjacent, or within the areas they belong. This course is designed to get you started with geodatabase topology.After completing this course, you will be able to:Use visual inspection and topology to identify and correct errors.Build a geodatabase topology.Choose and apply topology rules.

The goal of this paper is to present a new model of fuzzy topological relations for simple spatial objects in Geographic Information Sciences (GIS). The concept of computational fuzzy topological space is applied to simple fuzzy objects to efficiently and more accurately solve fuzzy topological relations, extending and improving upon previous research in this area. Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on computational fuzzy topology. And then, we also propose a new model to compute fuzzy topological relations between simple spatial objects, an analysis of the new model exposes:(1) the topological relations of two simple crisp objects; (2) the topological relations between one simple crisp object and one simple fuzzy object; (3) the topological relations between two simple fuzzy objects. In the end, we have discussed some examples to demonstrate the validity of the new model, through an experiment and comparisons of existing models, we showed that the proposed method can make finer distinctions, as it is more expressive than the existing fuzzy models.

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.

Countywide Surface Topology

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

Mature Support Notice: This item is in mature support as of June 2021. A replacement item has not been identified at this time.This map presents land cover and detailed topographic maps for the United States. It uses the USA Topographic Map service. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps.The maps provide a very useful basemap for a variety of applications, particularly in rural areas where the topographic maps provide unique detail and features from other basemaps.To add this map service into a desktop application directly, go to the entry for the USA Topo Maps map service. Tip: Here are some famous locations as they appear in this web map, accessed by including their location in the URL that launches the map:Grand Canyon, ArizonaGolden Gate, CaliforniaThe Statue of Liberty, New YorkWashington DCCanyon De Chelly, ArizonaYellowstone National Park, WyomingArea 51, Nevada

Residual biomass is a renewable resource with attractive characteristics to produce energy and biofuels. Diverse studies have stated that residual biomass used for biofuels and energy production can contribute partially to solve the energy demand problem, decreasing fossil fuels carbon emissions. Most works have focused on developing new technologies, processes, and processing systems based on biomass. Other works have addressed the supply chain-planning problem to determine optimal locations considering diverse objectives. A third group of works have proposed schemes based on Geographic Information Systems (GIS) to determine suitable locations in different types of systems. Nevertheless, works capable to combine the advantage of GIS, mathematical programming, and process design have not been properly conducted. Therefore, this paper presents a sequential approach for the optimal planning of a residual biomass processing system. The methodology considers selecting potential locations through a multicriteria methodology based on GIS. Also, this paper proposes a mathematical programming approach for the optimal planning of a residual biomass processing system, which considers as input the locations predefined by GIS methodology, as well as six potential products, six processing routes, and eight raw materials. The mathematical programming approach consists of mass balances to obtain the interconnections between the different supply chain nodes, as well as constraints to model the considered technologies involving capital investment and production costs. The GIS approach was applied to a case study in Mexico, which produced 764 harvesting sites and 334 processing plants for all considered residual biomass types. The optimization approach conducted used 33 processing plants, 467 harvesting sites, and 2 products from 3 biomass types in order to determine the final supply chain topology. Results show that the proposed methodology is a useful tool to determine the optimal supply chain topology during the decision process.

This Administration feature is the single most valuable feature maintained by the GIS Services staff. It combines the maintenance of many individual polygon features in one main overall feature.It is part of a ArcGIS Topology class maintained with our parcel and zoning features in the Editing Feature Data Set.We use the shared editing capabilities of this topology class to leverage our maintenance procedures as simply as possible. Weekly, the individual features maintained with our Administration feature are created with ArcGIS dissolve function. These include Jurisdiction boundaries, Public Safety Response areas, Voting Precincts, Schools Attendance Zones, Inspections, Library Service Zones, and more.Generally, maintenance of this feature is controlled thru shared editing performed with our parcel/zoning edits with the use of the Topology features in ArcGIS. Changes to features maintained in the Administration feature are caused by a number of issues. Parcel edits, new Public Safety Stations, changes in Voting Precincts, Police Reporting districts and other changes occur often. Most changes can be facilitated by selecting one or more “Administrative” polygons and changing the appropriate attribute value. Use of the “Cut Polygon” task may be necessary in those cases where part of a polygon must be changed from a district to another. The appropriate attribute can be changed in the affected area as necessary.

Table of all categorized mentions of places in the testimonies transcripts of Anna Patipa and Jacob Brodman.

Background

The soil map of Israel was first published by Rabinovitch et al. in 1969. It was a massive work that took place 5 years. The map was published in a printed format at a 1:250,000 scale. Until now, a digital version of this map was not available. Accordingly, we carefully digitized the soil map of Rabinovitch and provided the map herein.

Materials and Methods

This dataset contains georeferenced raster layers of the soil map (1:250,00) of Israel published by Ravikovitch (1969). The georectification was done using control points located on the borders of Israel. With this information, it was possible to create polygons over the georeferenced raster layers. This was done using the editing tool of ArcGIS 10.3. For each polygon we assigned the same classification provided by Ravikovitch (1969). Once all the polygons were created, topological corrections were applied using the method of Longley et al., (2015) in order to rectify possible inaccuracies in the digitation. To this end, we used the topology tool of ArcGIS 10.3 applying two rules:

• polygons must not have gaps
• polygons must not overlap.

This publication contains:

• the northern and the southern sections of the Israel map of soils after the georectification in geotiff format
• the final product of the cartographic edition of the Israel map of soils in shapefile format
• a PDF map showing the shapefile layer over the original map after georectification

Please, if you are going to make use of this map, cite this publication properly.

References

• Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic information science and systems. John Wiley & Sons.
• Ravikovitch, S. (1969) Manual and Map of Soils of Israel; The Magnes Press, The Hebrew University: Jerusalem, Israel.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of sports facility

EditDate type: Stringwidth: 4precision: 0

This polygon feature class is a data set compiled by DWR employees in 2013 and represents the statewide Groundwater Management Plan (Plan) boundaries predating the Sustainable Groundwater Management Act (SGMA) requirements. Each polygon represents the area in which a Plan is to be implemented. The boundaries were provided to DWR by the affiliated public agency and compiled into a single statewide data set. Spatial plan boundaries were provided by agencies to DWR either via shapefiles or PDFs. PDFs were georeferenced and turned into GIS layers by DWR employees. This feature class is for legacy purposes only and will not be changed nor updated. It needs to be memorialized for spatial coverage of Groundwater Management Plans prior to SGMA and because SGMA only requires medium and high priority basins to have a Groundwater Sustainability Plan. The Plans outlined in this shapefile by medium and high priority basins are in effect until SGMA goes into effect. Some low and very low priority basins will likely use the existing plans to get funding for future basin management (since it is only voluntary for them to provide a Plan under SGMA, but they already have one in place). The data set is considered complete because of its legacy status. However, anyone using the data set will notice boundary inconsistencies, agency plan overlaps, mismatches, and other topology errors. The data set is based on boundary estimations and in the cases of medium and high priority basins will be outdated with in implementation of SGMA.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.1, dated September 11, 2019. This data set was not produced by DWR. Data were originally developed and supplied by each individual plan agency and compiled by DWR. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

Overview of topology editing workflow in ArcMap.

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data. The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data. The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology: Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the island data provided by the State Department to polygons. The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources. The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset. The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines. The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles. Once all topological errors were fixed, the lines were converted to polygons. Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset. The polygons were then dissolved by country name. Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly. Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS. The same procedure was performed for every individual island contained in the islands from other USG sources.
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset. The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.

This is a national data collection of data resources managed by the Bureau of Ocean Energy Management (BOEM) for the Outer Continental Shelf (OCS). The data collection is designated as a National Geospatial Data Asset (NGDA) and includes: OCS BOEM Offshore Boundary Lines (Submerged Lands Act Boundary, OCSLA Limit of “8(g) Zone,” and Continental Shelf Boundary), OCS Protraction Polygons - 1st Division, OCS Gulf of Mexico NAD27 Protraction Polygons - 1st Division, OCS Block Polygons - 2nd Division, OCS Gulf of Mexico NAD27 Block Polygons - 2nd Division, and Aliquot 16ths Polygons - 3rd Division.All polygons are clipped to the Submerged Land Act Boundary and Continental Shelf Boundaries reflecting federal jurisdiction. The NAD27 Gulf of Mexico Protractions and Blocks have a different protraction and block configuration when compared to the OCS Protraction Polygons - 1st Division and OCS Block Polygons - 2nd Division. The NAD27 Gulf of Mexico data is used for Oil and Gas leasing.These data were created in the applicable NAD83 UTM or NAD27 UTM/SPCS Projection and re-projected to GCS WGS84 (EPSG 4326) for management in BOEM"s enterprise GIS. However, the services in this collection have been published in WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857). Because GIS projection and topology functions can change or generalize coordinates,these data are NOT an OFFICIAL record for the exact boundaries. These data are to be used for Cartographic purposes only and should not be used to calculate area.Layers MetadataOCS BOEM Offshore Boundary LinesOCS Protraction Polygons - 1st DivisionOCS Gulf of Mexico NAD27 Protraction Polygons - 1st DivisionOCS Block Polygons - 2nd DivisionOCS Gulf of Mexico NAD27 Block Polygons - 2nd DivisionAliquot 16ths Polygons - 3rd Division

This data was created in the applicable NAD83 UTM Projection and re-projected to NAD83 Geographic. Because GIS projection and topology functions can change or generalize coordinates, these GIS files are NOT an OFFICIAL record for the exact boundaries. These files are to be used for Cartographic purposes only.

ABSTRACT Watershed delineation, drainage network generation and determination of river hydraulic characteristics are important issues in hydrological sciences. In general, this information can be obtained from Digital Elevation Models (DEM) processing within GIS commercial softwares, such as ArcGIS and IDRISI. On the other hand, the use of open source GIS tools has increased significantly, and their advantages include free distribution, continuous development by user communities and full customization for specific requirements. Herein, we present the IPH-Hydro Tools, an open source tool coupled to MapWindow GIS software designed for watershed topology acquisition, including preprocessing steps in hydrological models such as MGB-IPH. In addition, several tests were carried out assessing the performance and applicability of the developed tool, given by a comparison with available GIS packages (ArcGIS, IDRISI, WhiteBox) for similar purposes. The IPH-Hydro Tools provided satisfactory results on tested applications, allowing for better drainage network and less processing time for catchment delineation. Regarding its limitations, the developed tool was incompatible with huge terrain data and showed some difficulties to represent drainage networks in extensive flat areas, which can occur in reservoirs and large rivers.