The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

The Geographic Management Information System (GeoMIS) is a FISMA Moderate minor application built using ArcGIS Server and portal, Microsoft SQL, and a web-facing front-end. The system can be accessed over the internet via https://www.usaidgiswbg.com using a web browser. GeoMIS is based on a commercial off-the-shelf product developed by Esri. Esri is creates geographic information system (GIS) software, web GIS and geodatabase management applications and is based in California. GeoMISIt is maintained by an Israeli company, Systematics (see Attachment 3) which is EsriI's agent in Israel. The mission has an annual maintenance contract with Systematics for GeoMIS. GeoMIS has 100 users from USAID staff (USA Direct Hire and Foreign Service Nationals) and 200 users from USAID contractors and grantees. The system is installed at USAID WBG office in Tel Aviv/Israel inside the computer room in the DMZ. It has no interconnections with any other system.

JSONLayer: City Limits (ID:0)Name: City LimitsDisplay Field: CITYType: Feature LayerGeometry Type: esriGeometryPolygonDescription: Digital, spatially georeferenced graphic representation of the city limits of the City of Dallas, Texas.Copyright Text: City of Dallas GIS ServicesMin. Scale: 577792Max. Scale: 0Default Visibility: trueMax Record Count: 1000Supported query Formats: JSONUse Standardized Queries: TrueExtent:XMin: 2430144.56888622YMin: 6909399.42103373XMax: 2592859.12567431YMax: 7061339.44851789Spatial Reference: 102738 (2276)Drawing Info:{"renderer":{"type":"simple","symbol":{"type":"esriSFS","style":"esriSFSSolid","color":[115,156,156,255],"outline":{"type":"esriSLS","style":"esriSLSSolid","color":[115,156,156,255],"width":1}},"label":"","description":""},"transparency":0,"labelingInfo":null}HasZ: falseHasM: falseHas Attachments: falseHas Geometry Properties: trueHTML Popup Type: esriServerHTMLPopupTypeAsHTMLTextObject ID Field: OBJECTIDUnique ID Field:Name : OBJECTIDIsSystemMaintained : TrueGlobal ID Field:Type ID Field:Fields:OBJECTID (type: esriFieldTypeOID, alias: OBJECTID, SQL Type: sqlTypeOther, length: 0, nullable: false, editable: false)CITY (type: esriFieldTypeString, alias: CITY, SQL Type: sqlTypeOther, length: 20, nullable: true, editable: true)Shape_Area (type: esriFieldTypeDouble, alias: Shape_Area, SQL Type: sqlTypeDouble, nullable: true, editable: false)Shape_Length (type: esriFieldTypeDouble, alias: Shape_Length, SQL Type: sqlTypeDouble, nullable: true, editable: false)Templates:Name: City LimitsDescription:Drawing Tool: esriFeatureEditToolPolygonPrototype:Attributes:Is Data Versioned: falseSupports Rollback On Failure Parameter: trueLast Edit Date: 7/29/2020 3:52:44 PMSupported Operations: Query Query Top Features Query Analytic Generate Renderer Validate SQL

The Calls for Service dataset includes police service requests for which patrol officers, traffic officers, bike officers and, on occasion, detectives will be dispatched to public safety response. It also includes self-initiated calls for service where an officer witnesses a violation or suspicious activity for which they would respond.Contact E-mailContact Phone: N/ALink: N/AData Source: Versaterm Informix RMSData Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from CADWSQL (SQL Server) and duplicated on the GIS ServerPublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

Contact E-mail Contact Phone: N/A Link: N/A Data Source: Versaterm Informix RMS Data Source Type: Informix and/or SQL Server Preparation Method: Preparation Method: Automated View pulled from CADWSQL (SQL Server) and duplicated on the GIS Server Publish Frequency: Weekly Publish Method: Automatic Data Dictionary

Line features representing existing and proposed fiber lines (buried & aerial) owned by Chatham County MIS in Chatham County, NC. These fiber lines are utilized for the NC811 notification system that Chatham County MIS participates in.

The buried fiber line features are utilized to create a buffer polygon that is imported into NC811's notification system serving as the basis for all Chatham County MIS notifications. The original data was collected by Performance Cabling using industry standard GPS collection methods. The data was delivered to Chatham County MIS / GIS in May of 2015. The data was imported into the ChathamGIS SQL database in August 2015 and stored in the "infrastructure" dataset.

The ongoing data updates and maintenance will be conducted by Chatham County GIS in collaboration with Performance Cabling on an as needed basis.Chatham GIS SOP: "MAPSERV-74"

The Kid Zone Enrichment Program provides a safe and enriching place for students to be in out-of-school time. This dataset provides the percentage of students who are currently enrolled in after school community programs. Data are broken down by school, including school and Kid Zone enrollment.

This page provides data for the Kid Zone Participation performance measure.

These data are the source of the summary values for Performance Measure 1.18

The performance measure dashboard is available at 1.18 Kid Zone Participation

Additional Information

Source: SQL Server

Contact: Jeremy King

Contact E-Mail: jeremy_king@tempe.gov

Data Source Type: SQL Server

Preparation Method: Extracted to Excel and combined with data given by the Tempe and Kyrene School districts (school enrollment, Free and Reduced Lunch Percentages). These data are generated from from SQL counts the participants by site with an enrollment end date greater or equal to the report date.

Publish Frequency: Annually

Publish Method: Manual

Data Dictionary

Affiliate Associations consists of a group of non-profits or organizations involving business members or residents comprised of multiple neighborhoods with a common purpose and interest in neighborhoods. The data imported in this feature layers highlights the nine organizations that make up Tempe’s Affiliate Associations. They are represented as points when utilized. Contact: Will DukeContact E-Mail: will_duke@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

The General Offense Crime Report Dataset includes criminal and city code violation offenses which document the scope and nature of each offense or information gathering activity. It is used to computate the Uniform Crime Report Index as reported to the Federal Bureau of Investigation and for local crime reporting purposes.Contact: Carlena Orosco - Police Planning and Research SupervisorContact E-mailLink: N/AData Source: Versaterm Informix RMS - CADWSQLData Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from CADWSQL (SQL Server) and duplicated on the GIS ServerPublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

A polygon depiction of property sales from 2010 to the present that occurred in Stark County, Ohio. The Stark County Auditor's Office (SCAO) maintains records of property sales using a Computer-Assisted Mass Appraisal (CAMA) Database. This layer is a SQL view combining the sales records from the CAMA database with the Stark County parcel layer. A new view is created every morning through a combination of python scripts and SQL stored procedures. The data always reflects the most-recent information available from the previous day for both sources.

Associations created to maintain the quality of life in a given neighborhood. These associations consist of both neighborhood associations (NA) and homeowner associations (HOA).Contact E-Mail: jacob_payne@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: Automatic Data Dictionary

This example demonstrates how to use PostGIS capabilities in CyberGIS-Jupyter notebook environment. Modified from notebook by Weiye Chen (weiyec2@illinois.edu)

PostGIS is an extension to the PostgreSQL object-relational database system which allows GIS (Geographic Information Systems) objects to be stored in the database. PostGIS includes support for GiST-based R-Tree spatial indices, and functions for analysis and processing of GIS objects.

Resources for PostGIS:

Manual https://postgis.net/docs/ In this demo, we use PostGIS 3.0. Note that significant changes in APIs have been made to PostGIS compared to version 2.x. This demo assumes that you have basic knowledge of SQL.

This data has been collected as part of a larger project by the City of Austin's Watershed Protection and Development Review Department to inventory its drainage infrastructure and create a GIS to store this information. The project includes an internal team developing a GIS based on record documents and an external team locating ground level appurtenances using GPS field collection units. The data in this data set represents the former.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenance for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best available data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.Full details: https://geohub-oregon-geo.hub.arcgis.com/datasets/oregon-geo::oregon-highway-network/about

Moving citation locations in the District of Columbia. The Vision Zero data contained in this layer pertain to moving violations issued by the District of Columbia's Metropolitan Police Department (MPD) and partner agencies with the authority. For example, DC's enforcement camera program cites speeders, blocking the box, and other moving offenses.. Moving violation locations are summarized ticket counts based on time of day, week of year, year, and category of violation.Data was originally downloaded from the District Department of Motor Vehicle's eTIMS meter work order management system. Data was exported into DDOT’s SQL server, where the Office of the Chief Technology Officer (OCTO) geocoded citation data to the street segment level. Data was then visualized using the street segment centroid coordinates.

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.

Contains approximately 38,447 point locations of Wyoming well permit locations on file with the Wyoming State Engineer's Office. The wells have been located to the to the nearest 40 acre parcel. All locational information and attributes were imported from the Wyoming State Engineer's Office Well Permits Database stored in SQL-Server