Analysis of ‘2018 CT Data Catalog (GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5a93e011-4ea8-40b1-a888-0f573e6b785d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog contains information on high value GIS data only. A catalog of high value non-GIS data may be found at the following link: https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 1/2/2019 and will continue to be updated as high value data inventories are submitted to OPM.

--- Original source retains full ownership of the source dataset ---

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Analysis of ‘2019 CT Data Catalog (Non GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/63dbeae9-0f9d-41d7-9ad9-edc2e4fdea74 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management, updated in 2019. This catalog does not contain information about high value GIS data, which is compiled in a separate data inventory at the following link: https://data.ct.gov/Government/2019-CT-Data-Catalog-GIS-/kr39-sdfm

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 2/6/2020 and will continue to be updated as high value data inventories are submitted to OPM.

The 2018 high value data inventories for Non-GIS and GIS data can be found at the following links: CT Data Catalog (Non GIS): https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn/ CT Data Catalog (GIS): https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27

--- Original source retains full ownership of the source dataset ---

The Office of the Chief Technology Officer (OCTO), within the District of Columbia (DC) government, manages the District’s data program. This includes open data, data curation, data integration, data storage, data science, data application development and Geographic Information Systems (GIS). The open data handbook explains the process and steps OCTO undertakes when an agency submits an open dataset for publication. The handbook outlines dataset rules, documentation requirements, and policies to make data consistent and standardized. This applies to any dataset submitted for publication on the Open Data DC portal that is classified as Level 0: Open as defined in the District’s Data Policy. For previous versions of the handbook visit https://opendata.dc.gov/pages/handbook.

Become an ArcGIS Hub Specialist.ArcGIS Hub is a cloud-based engagement platform that helps organizations work more effectively with their communities. Learn how to use ArcGIS Hub capabilities and related technology to coordinate and engage with external agencies, community partners, volunteers, and citizens to tackle the projects that matter most in your community._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The dataset contains locations and attributes of Post Offices, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Information provided by the United States Postal Service (USPS) and DC GIS staff geo-processed the data.

This dataset contains locations and attributes of University and College, created as part of the DC Geographic Information System (DC GIS) for the Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Information provided by OCTO, EMA, and other sources identified as University Areas and DC GIS staff geo-processed the data. This layer does not represent university areas contained in the campus plans from the DC Office of Zoning.

The dataset contains locations and attributes of Embassies, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the DC Office of the Chief Technology Officer (OCTO) identified Embassy locations and DC GIS staff geo-processed the data to the Master Address Repository (MAR).

Overview of Crisis Intervention Team data, including call types, locations, time of day, demographics, de-escalation, Specialized CIT officer involvement, resistance, and use of force. The dataset reflects information gathered from completed CIT Data Collection Forms.This dataset is connected to: Crisis Intervention Team DashboardCleveland's Crisis Intervention Program StoryMapUpdate FrequencyData will be updated annually and become available in the 2nd quarter of the subsequent year. For example, 2024 CIT data will be available in the 2nd quarter of 2025.Contact InformationCleveland Division of Police Bureau of ComplianceData Analysis UnitData GlossaryColumn | DescriptionRecord_Number | Unique identifier for each Crisis Intervention form.Date | Date the event occurred. Stored as text.Year | Year the event occurred. Data contains a comma due to settings in ArcGIS.Month | Month the event occurred (numeric; 1 - January, 2 - February, etc.).Day_of_Month | The numeric day of the month in which the event occurred.Day_of_Week | Day of the week the event occurred.Hour_Rounded | Hour of the day the event occurred, rounded to the nearest hour. When minutes are between 00 and 30, the time is rounded back (e.g., 8:13 p.m. is rounded to 8:00 p.m.). When minutes are between 31 and 59, the time is rounded forward (e.g., 10:42 a.m. is rounded to 11:00 a.m.).Police_District | Cleveland Division of Police District where the event occurred. (1; 2; 3; 4; 5)Call_Source | From where or whom the call originated. (Dispatch Center; Officer Initiated)Call_Type | The initial Computer Aided Dispatch (CAD) classification when the call was first generated.Level_of_Resistance | The highest level of resistance by the individual. (No Resistance; Passive Resistance; Active Resistance; Aggressive Physical Resistance)Level_of_Force | The level of force by the officer. (No Force Used, Level 1 UOF, Level 2 UOF, Level 3 UOF)Individual_Arrested | Whether or not the individual was arrested during the event.Individual_Cited | Whether or not the individual received a citation during the event.DeEscalation_Used | Whether or not the officer used de-escalation.Specialist_CIT_Officer_On_Scene | Indicates if either officer 1 or officer 2 (if applicable) or the supervisor on scene (if applicable) was a Specialized Crisis Intervention Team officer.Officer_Injured | Indicates if the officer was injured during the crisis intervention event.Officer_Injury_Force_Related | Indicates if the injury an officer received during the crisis intervention event was related to a use-of-force.Individual_Injured | Indicates if the individual was injured during the crisis intervention event.Individual_Injury_Force_Related | Indicates if the injury an individual received during the crisis intervention event was related to a use-of-force.Individual_Veteran | Whether or not the individual was a veteran.Individual_Sex | Sex of the individual. (Female; Male, Unknown)Individual_Race | Race of the individual. (American Indian or Alaskan Native; Asian; Black or African American; Native Hawaiian or Other Pacific Islander; Unknown, White)Individual_Youth | Whether or not the individual is under 18 years of age.Individual_Ethnicity | Ethnicity of the individual. (Hispanic, Non-Hispanic, Unknown)Individual_Referred_for_Additional_Support | Whether or not the individual was referred for additional support services.

A collection of Business Analyst resources to assist in responding to COVID-19.Many communities have been impacted by the spread of the novel coronavirus and the symptoms of coronavirus disease 2019 (COVID-19). The ArcGIS Business Analyst team has gathered resources for using Business Analyst to respond._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Analysis of ‘PLACES: County Data (GIS Friendly Format), 2020 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d85c2f1c-0aa1-4eb6-a383-7a82f4aa7f6b on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

--- Original source retains full ownership of the source dataset ---

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset gives the extents …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset gives the extents of South Australian pastoral lease stations and other properties within the pastoral region of SA. The extents of the properties shown are based on the areas being managed by the leasees and boundaries are defined by fence lines rather than legal lease boundaries. Fences and legal lease boundaries are frequently divergent. Purpose This dataset will show the extents of South Australian pastoral lease stations within the pastoral region of SA. Dataset History Assessment/Inspection officers visit each station and drive around the property using a mobile device with GPS capability for field data entry including tracking and waypoints of features. The station owner/manager also contribute new information. Maps are often used to explain complex fencing changes. The GIS officer is responsible for adding the changes collected in the field into the database using ESRI ArcGIS software. Imagery and GoogleEarth are often used to verify data collected however often this is based on 2007 or older imagery.Linework is based on data captured from a variety of sources, some of which are not known. Dataset Citation SA Department of Environment, Water and Natural Resources (2015) Pastoral Stations - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/22ce4795-d3a9-432a-89a7-8fe53391d50d.

Primary care centers where residents can find available health care services in the District of Columbia. The dataset contains locations and attributes of Primary Care Centers, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the DC Department of Health (DOH) identified Primary Care Centers and DC GIS staff geo-processed the data.

The TPD Incidents of Aggravated Assaults Against Police Officers, 2010 - 2015 file contains incidents that occurred between January 1, 2010 and December 31, 2015, against an Officer of the Tucson Police Department while on-duty and engaged in a call for service. PurposeThis data is provided for informational purposes only. Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsTotals in this file may vary from official totals following investigation. This data is not to be relied upon for official purposes. There is a small margin of error when processing the data for mapping purposes; therefore, some incidents may either be omitted from, or inaccurately mapped. On July 31, 2012, TPD transitioned to a completely new computer-aided dispatch and records management system, therefore some fields in this dataset will not be populated prior to this date.Data ContactTucson PoliceUpdate FrequencyUpdated OccasionallyLast known update: April 2016

2 foot contours (2008) provided as shapefile. This dataset may delay in downloading. Optionally download geodatabase. This dataset contains locations and attributes of 2-ft interval topography data, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. In addition to the 2-ft contour data ancillary datasets containing an ESRI geodatabase of masspoints and breaklines.

Regional Evacuation Routes. This dataset contains contains points representing locations of Regional Evacuation Routes, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Files provided by the District Department of Transportation contained DC, beltway, and regional evacuation routes. OCTO merged these layers together to form one layer.

Hospitals. This dataset contains points representing hospital locations, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Hospital locations were identified from public records and heads-up digitized from the snapbase.

The dataset contains locations and attributes of Cemeteries, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database from OCTO identified Cemetery locations and DC GIS staff geo-processed the data.