The dataset contains locations and attributes of owner lines with dimensions. The tax information (attribution) comes from the Office of Tax and Revenue's Public Extract file. The creation of this layer is automated, occurs weekly, and uses the most currently available tax information. The date of the extract can be found in the EXTRACTDAT field in this layer.

Line geometry with attributes displaying ownership of street centerline segments in East Baton Rouge Parish, Louisiana.

Monthly extracts of historic Traffic Data at Signalised derived by SCATS.

SCATS (Sydney Coordinated Adaptive Traffic System) is an intelligent transportation system that manages the dynamic timing of signal phases at traffic signals in real time. The system estimates the number of vehicles passing through the intersection and other information related to traffic signal timing. There is no guarantee this data is accurate or was used to make internal decisions in SCATS.

The data is provided by controller site. Each site has its own parquet file for the month, which contains SCATS data produced by that site. The files use the LM site number format (e.g. – Site 1 is LM00001).

Note that you are accessing the data provided by the links below pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes and may have errors.

Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”

A data dictionary is provided at the document link.

Monthly data extracts are in parquet format.

The locations of the traffic signals are found at the link below.

https://portal-mainroads.opendata.arcgis.com/datasets/traffic-signal-sitesAvailable in JSON format below.gisservices.mainroads.wa.gov.au/arcgis/rest/services/Connect/MapServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pjson

The mapping of the detectors to the strategic approaches at an intersection is given at the link below.

https://mainroadsopendata.mainroads.wa.gov.au/swagger/ui/index#/LmSaDetector

Further information, including SCATS graphics, is available via the Traffic Signal information on Main Roads TrafficMap

trafficmap - Main Roads WA

NOTE: The Historic Traffic Data Dashboard & Feature Hosted Service have been retired.Network operations traffic data from Main Roads Western Australia from 2013 onwards. The data provided includes data collected on the Perth Metropolitan State Road Network (PMSRN) at 15 minute intervals.

The Historic Traffic Data is provided in CSV format per year. Each table has over 34 million rows and can be linked to the M-Links Road Network using the M-Links ID. A data dictionary for M-Links Road Network and the Historic Traffic Data is at the following link:https://mainroads.sharepoint.com/:w:/s/mr-opendata/EVHlw9Ils59Al4q3y7xxWxABBSOHVr4SCrxOYzJw1dReQg?e=KUhjhb

The network operations traffic data provided here is of variable quality and has not been checked, quality assured or manually corrected. An automated process is used to patch over missing or suspect data with the most representative data available within the database. Patches may be reapplied as new data becomes available and patched data may change over time.

Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.

Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market's value is estimated at $2 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of location-based services, the proliferation of readily available geographic data, and the growing need for effective data visualization in business intelligence and marketing. The individual user segment currently holds a significant share, but corporate adoption is rapidly expanding, propelled by the need for sophisticated map-based analytics and internal communication. Furthermore, the paid use segment is anticipated to grow more quickly than the free use segment, reflecting the willingness of businesses and organizations to invest in advanced features and functionalities. This trend is further amplified by the increasing integration of interactive maps into various platforms, such as business intelligence dashboards and website content. Geographic expansion is also a significant growth driver. North America and Europe currently dominate the market, but the Asia-Pacific region is showing significant promise due to rapid technological advancements and increasing internet penetration. Competitive pressures remain high, with established players such as Google, Mapbox, and ArcGIS StoryMaps vying for market share alongside innovative startups offering specialized solutions. The market's restraints are primarily focused on the complexities of data integration and the technical expertise required for effective map creation. However, ongoing developments in user-friendly interfaces and readily available data integration tools are mitigating these challenges. The future of the interactive map creation tools market promises even greater innovation, fueled by developments in augmented reality (AR), virtual reality (VR), and 3D visualization technologies. We expect to see the emergence of more sophisticated tools catering to niche requirements, further driving market segmentation and specialization. Continued investment in research and development will also play a crucial role in pushing the boundaries of what's possible with interactive map creation. The market presents opportunities for companies to develop tools which combine data analytics and interactive map design.

For more information about this tool see Batch Metadata Modifier Tool Toolbar Help.Modifying multiple files simultaneously that don't have identical structures is possible but not advised. Be especially careful modifying repeatable elements in multiple files that do not have and identical structureTool can be run as an ArcGIS Add-In or as a stand-alone Windows executableExecutable runs on PC only. (Not supported on Mac.)The ArcGIS Add-In requires ArcGIS Desktop version 10.2 or 10.3Metadata formats accepted: FGDC CSDGM, ArcGIS 1.0, ArcGIS ISO, and ISO 19115Contact Bruce Godfrey (bgodfrey@uidaho.edu, Ph. 208-292-1407) if you have questions or wish to collaborate on further developing this tool.Modifying and maintaining metadata for large batches of ArcGIS items can be a daunting task. Out-of-the-box graphical user interface metadata tools within ArcCatalog 10.x are designed primarily to allow users to interact with metadata for one item at a time. There are, however, a limited number of tools for performing metadata operations on multiple items. Therefore, the need exists to develop tools to modify metadata for numerous items more effectively and efficiently. The Batch Metadata Modifier Tools toolbar is a step in that direction. The Toolbar, which is available as an ArcGIS Add-In, currently contains two tools. The first tool, which is additionally available as a standalone Windows executable application, allows users to update metadata on multiple items iteratively. The tool enables users to modify existing elements, find and replace element content, delete metadata elements, and import metadata elements from external templates. The second tool of the Toolbar, a batch thumbnail creator, enables the batch-creation of the graphic that appears in an item’s metadata, illustrating the data an item contains. Both of these tools make updating metadata in ArcCatalog more efficient, since the tools are able to operate on numerous items iteratively through an easy-to-use graphic interface.This tool, developed by INSIDE Idaho at the University of Idaho Library, was created to assist researchers with modifying FGDC CSDGM, ArcGIS 1.0 Format and ISO 19115 metadata for numerous data products generated under EPSCoR award EPS-0814387.This tool is primarily designed to be used by those familiar with metadata, metadata standards, and metadata schemas. The tool is for use by metadata librarians and metadata managers and those having experience modifying standardized metadata. The tool is designed to expedite batch metadata maintenance. Users of this tool must fully understand the files they are modifying. No responsibility is assumed by the Idaho Geospatial Data Clearinghouse or the University of Idaho in the use of this tool. A portion of the development of this tool was made possible by an Idaho EPSCoR Office award.

Neighborhoods: Home Owner Associations is a Polygon FeatureClass representing neighborhoods as designated by the Home Owners Association. It is primarily used as a reference layer. The layer is updated as needed by the GIS Division. Neighborhoods: Home Owner Associations has the following fields:

OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

ManagementCo: The management company type: String, length: 100, domain: none

ManageAddress: The management company address type: String, length: 100, domain: none

last_edited_date: The date the database row was last updated type: Date, length: 8, domain: none

created_date: The date the database row was initially created type: Date, length: 8, domain: none

isHOA: Field indicating whether the neighborhood is part of the Home Owners Association or not type: String, length: 3, domain: shdBooleanYesNo domain values:['Yes', 'No']

isNeighborhood: EnterTextDescription type: String, length: 3, domain: shdBooleanYesNo domain values:['Yes', 'No']

Name: Name of the neighborhood as designated by the Home Owners Association type: String, length: 100, domain: none

isApartment: Field indicating if the Home Owners Association neighborhood is an apartment complex type: String, length: 3, domain: shdBooleanYesNo domain values:['Yes', 'No']

IsCommercial: Field indicating whether or not the Home Owners Association neighborhood is commercial type: String, length: 3, domain: shdBooleanYesNo domain values:['Yes', 'No']

SHAPE: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

Shape.STArea():

The area of the shape - in square feet type: Double, length: 0, domain: none

Shape.STLength():

The length of the shape - in feet type: Double, length: 0, domain: none

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

The Geospatial Analytics Market size was valued at USD 98.93 billion in 2023 and is projected to reach USD 227.04 billion by 2032, exhibiting a CAGR of 12.6 % during the forecasts period. The Geospatial Analytics Market describes an application of technologies and approaches processing geographic and spatial data for intelligence and decision-making purposes. This market comprises of mapping tools and software, spatial data and geographic information systems (GIS) used in various fields including urban planning, environmental, transport and defence. Use varies from inventory tracking and control to route optimization and assessment of changes in environment. Other trends are the growth of big data and machine learning to improve the predictive methods, the improved real-time data processing the use of geographic data in combination with other technologies, for example, IoT and cloud. Some of the factors that are fuelling the need to find a marketplace for GIS solutions include; Increasing importance of place-specific information Increasing possibilities for data collection The need to properly manage spatial information in a high stand environment. Recent developments include: In May 2023, Google launched Google Geospatial Creator, a powerful tool that allows users to create immersive AR experiences that are both accurate and visually stunning. It is powered by Photorealistic 3D Tiles and ARCore from Google Maps Platform and can be used with Unity or Adobe Aero. Geospatial Creator provides a 3D view of the world, allowing users to place their digital content in the real world, similar to Google Earth and Google Street View. , In April 2023, Hexagon AB launched the HxGN AgrOn Control Room. It is a mobile app that allows managers and directors of agricultural companies to monitor all field operations in real time. It helps managers identify and address problems quickly, saving time and money. Additionally, the app can help to improve safety by providing managers with a way to monitor the location and status of field workers. , In December 2022, ESRI India announced the availability of Indo ArcGIS offerings on Indian public clouds and services to provide better management, collecting, forecasting, and analyzing location-based data. , In May 2022, Trimble announced the launch of the Trimble R12i GNSS receiver, which has a powerful tilt adjustment feature. It enables land surveyors to concentrate on the task and finish it more quickly and precisely. , In May 2021, Foursquare purchased Unfolded, a US-based provider of location-based services. This US-based firm provides location-based services and goods, including data enrichment analytics and geographic data visualization. With this acquisition, Foursquare aims to provide its users access to various first and third-party data sets and integrate them with the geographical characteristics. , In January 2021, ESRI, a U.S.-based geospatial image analytics solutions provider, introduced the ArcGIS platform. ArcGIS Platform by ESRI operates on a cloud consumption paradigm. App developers generally use this technology to figure out how to include location capabilities in their apps, business operations, and goods. It aids in making geospatial technologies accessible. .

Information linking dog classification to specific events (customer complaints).

Column_Info

Relationship

This table reference to table Dog_ComplaintThis table reference to table Owner_Classification_Event

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Conservation/Open Space & Recreation land within Dukes County - Only those Owned by miscellaneous Private Non-Profits, Private Individuals, or Howmowners Associations, or Unknown Owner are included in this 'view' of the data. This is not a complete database of all conservation and recreational land within Dukes County.See the source layer's description. Schema follows (mostly) that of MassGIS Protected and Recreational Open Space.

This layer represents the most recent parcels as recorded through deeds for the City. Since some parcels have multiple owners (e.g. Condos), this data set includes a point feature representing each. Real property zoning and addresses are assigned by Neighborhood Development Services (NDS) and provided to City Assessor staff for entry into the Real Estate system (CAMA). The information contained in this file is NOT to be construed or used as a "legal description" of any parcel. Any perceived errors or omissions should be reported to the City Assessor's Office for correction.

This data is intended for read-only use. Land and Water Conservation Fund (LWCF) data from surface ownership fund table is attached to surface ownership to create a base layer that is used in Forest Service business functions, as well as by other entities such as states, counties, other agencies, and partners. This layer depicts only the Forest Service lands that are acquired through purchase, exchange, donation, and transfer that used LWCF-designated funds. It is not a complete representation of all Forest Service land acquisitions; only those that used LWCF-designated funds. Metadata and DownloadsThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Power plant capacity data and map are from the California Energy Commission. Map depicts total capacity of utility-scale power plant related at 1MW or more based on owner classification (federal, state, investor-owned utility, public-owned electric utility, and merchant). Counties without symbols had no utility-scale plants. Data is from 2021 and is current as of August 19, 2022. Projecting: NAD 1893 (2011) California (Teale) Albers (Meters). For more information contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747.

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

SLKs or Straight Line Kilometers are a location reference system used by Main Roads Western Australia to define the location of features or events along a road. This layer shows SLK points (every 100m) along the left and single carriageways for all State and Local Roads, Main Roads controlled paths contained within the road centreline of Main Roads road asset database. The purpose of this layer is to identify and label the measure of SLK along a particular route and is provided for information only. The SLK points are based on geometric measure and include points of equation. This means that you may notice “jumps” in the SLK between two points that are not equal to 100m. This is as a result of network changes that have been incorporated into the spatial road centr eline.A point of equation is a business term used to describe a point on the road network, which has two SLK references, one for the section leading up to it, and one for the section leaving from it. A Point of Equation is either: • A gap, which occurs when the new deviation/realignment is built shorter than the existing road. An SLK range is missing. • An overlap, which occurs when the new deviation/realignment is built longer than the existing road. An SLK range is duplicatedNote that you are accessing this data pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Creative Commons CC BY 4.0Data Dictionary

Field Name
Type
Description


ObjectID
OID
System generated unique number, overridden on data update


Shape
Point M
System generated identifier for shape type


RoadCway
Text,255
Concatenation of Main Roads Road Number and Carriageway columns. Used to generate unique routes per road carriageway combination.


SLKMin
Double
The maximum SLK (measure) of the RoadCway


SLKMax
Double
The maximum SLK (measure) of the RoadCway


SLKGen
Double
The SLK measure generated at 100m intervals (0.1, 0.2 etc..)


POINT_X
Double
The Longitude of the SLK point


POINT_Y
Double
The latitude of the SLK point


StreetviewURL
Text.100
A Google Maps Streeview URL link to the SLK point on the road


RoadSLK
Text,50
A concatenation of the RoadCway and SLKGen columns


Road_Name
Text, 80
The Main Roads route name of the road


Common_Usage_Name
Text, 80
The name of the road


RN_SLK
Text, 100
A concatenation of the Road_Name and SLKGen columns


CN_SLK
Text, 100
A concatenation of the Common Usage Name and SLKGen columns

Property boundaries of City owned parcels.

This dataset shows the road centerlines in Allegheny County.If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.Category: Civic Vitality and GovernanceOrganization: Allegheny CountyDepartment: Department of Computer ServicesTemporal Coverage: currentData Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey FootDevelopment Notes: noneOther: noneRelated Document(s): Addressing Data ModelFrequency - Data Change: As neededFrequency - Publishing: As neededData Steward Name: Deb BeiberData Steward Email: deborah.beiber@alleghenycounty.us

Parcels that are owned by the Federal Government.