This map shows the Housing and Urban Development Areas in Jackson County and was Map 12 in the Jackson County Community Fire Plan. The page size is 11 inches by 17 inches.

This dataset provides a complete and highly structured view of retail precincts across multiple regions, designed to support market analysis, location intelligence, retail expansion, and AI/ML modelling. It delivers information in multiple formats to accommodate a wide range of analytical, GIS, and business use cases, making it an essential resource for retail analysts, urban planners, investment teams, and data-driven decision-makers.

Included Data Files & Formats

1. Point File
2. All Precincts by Centroid (GeoJSON/Shapefile).
3. Each precinct is represented as a single point located at its geometric centroid.
4. Includes key attributes: id, precinct name. Ideal for quick visualisation, clustering, and spatial reference when boundary shapes are not required.

5. Supports applications such as proximity analysis, mapping, and location-based AI/ML models.

6. Polygon File – All Precincts by Polygon (GeoJSON/Shapefile)

7. Provides full precinct boundaries in polygon geometry for precise spatial representation.

8. Includes key attributes: id, precinct name.

9. Enables detailed GIS analysis, including area calculations, spatial overlays, and integration with mobility or demographic datasets.

10. Suitable for urban planning, retail network optimisation, trade area analysis, and catchment studies.

PDF – Precinct Reports (see attached sample) - Reports include comprehensive retail precinct insights across malls and high streets, showing retailer mix by category (F&B, Apparel, Fitness, Grocery, Health/Fitness, and more), catchment size, shopper origins, population, consuming class population and precinct ranking—designed to provide insights on store expansion opportunities. - Supports qualitative assessments, market research, and executive reporting.

1. Excel – Tabular Overview
2. Comprehensive spreadsheet with all precincts, including the following fields: ID, Precinct Name, Ranking, State, Country
3. Enables straightforward filtering, sorting, and integration with other datasets.
4. Useful for high-level analysis, reporting, and as a reference table for GIS mapping or AI models.

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976

[Metadata] 2020 Census Urban Areas for the State of Hawaii. Source: US Census Bureau, 2023. 2020 Census Urban Areas consist of 5,000 or more people or 2,000 or more housing units. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/uac20.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

GIS and PDF data set of the Urban Planning Documents of MARGES 26174 Local Urban Planning — PLU approved on 06/09/2018 enforceable on 08/10/2018 — FIN de VALIDITE: 26/11/2018 DPU update

Graphic data of Town Planning Board (TPB) Planning Guidelines No. 12C for Application for Developments within Deep Bay Area under Section 16 of the Town Planning Ordinance, including all geographical information system (GIS) data, data dictionary and guidelines on using the GIS data, provided by the TPB is available for download. Please note that in using the data, you have agreed to be bound unconditionally by the Terms and Conditions of Use of the digital planning data enclosed in the downloaded data. Please read carefully the Terms and Conditions of Use. Please click https://www.info.gov.hk/tpb/en/forms/Guidelines/pg12c_e.pdf to download the TPB Planning Guidelines No.12C. For details of the graphic data, please refer to Statutory Planning Portal 3 website (http://www.ozp.tpb.gov.hk). The multiple file formats are available for dataset download in API.

This hosted feature layer has been published in RI State Plane Feet NAD 83. THIS IS A FUTURE LAND USE PLANNING MAP CREATED IN 2006. THIS DOES NOT SHOW CURRENT 2025 LAND USE LAND COVER. The Land Use 2025 dataset was developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. It evolved from a GIS overlay analysis of land suitability and availability and scenario planning for future growth. The analysis focused on the 37% of the State identified as undeveloped and unprotected in a land cover analysis from RIGIS 1995 land use land cover data. The project studied areas for suitability for conservation and development, based on the location of key natural resources and public infrastructure. The results identified areas with future use potential, under three categories of development intensity and two categories of conservation.These data are presented in the Plan as Figure 121-02-(01), Future Land Use Map. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The intent of the Plan is to bring together the elements of the State Guide Plan such as natural resources, economic development, housing and transportation to guide conservation and land development in the State. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas.These data have several purposes and applications: They are intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, these data are a guide to assist the state and communities in making land use policies. It is important to note these data are a generalized portrayal of state land use policy. These are not a statewide zoning data. Zoning matters and individual land use decisions are the prerogative of local governments. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. It depicts where different intensities of development (e.g. parks, urban development, non-urban development) should occur by color. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the USB, most land is served by public water service; many areas also have public sewer service, as well as, public transit. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts.NOTE: These data will be updated when the associated plan is updated or upon an amendment approved by the State Planning Council. NOTE: Wetlands were not categorized within the Land Use 2025 dataset.When using this dataset, the RIGIS wetlands dataset should be overlaid as a mask. Full descriptions of the categories and intended uses can be found within Section 2-4, Future Land Use Patterns, Categories, and Intended Uses, of the Plan. https://www.planning.ri.gov/documents/guide_plan/landuse2025.pdf

Graphic data of Town Planning Board (TPB) Planning Guidelines No. 13G for Application for Open Storage and Port Back-up Uses under Section 16 of the Town Planning Ordinance, including all geographical information system (GIS) data, data dictionary and guidelines on using the GIS data, provided by the TPB is available for download. Please note that in using the data, you have agreed to be bound unconditionally by the Terms and Conditions of Use of the digital planning data enclosed in the downloaded data. Please read carefully the Terms and Conditions of Use. Please click https://www.info.gov.hk/tpb/en/forms/Guidelines/TPB_PG_13G_e.pdf to download the TPB Planning Guidelines No.13G. For details of the graphic data, please refer to Statutory Planning Portal 3 website (http://www.ozp.tpb.gov.hk). The multiple file formats are available for dataset download in API.

GIS and PDF data set of the Urban Planning Documents of the Local Urban Planning Plan of SAINTE JALLE 26306 — PLU approved on 04/07/2007 — Amendment No. 1 approved on 26/04/2010 enforceable on 08/07/2010 — FIN of VALIDITE: 30/06/2018 enforceability of Amendment No. 2 approved on 26/05/2018

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

GIS and PDF data set of the Urban Planning Documents of the Local Urban Planning Plan 26250 — PLU approved on 03/11/2008 — Simplified amendment No. 1 approved on 06/05/2019 — enforceability on 20/06/2019 — VALIDITE FIN: 23/12/2019 SIS update

GIS and PDF dataset of Urban Planning Documents in progress on the municipality of MUREILS 26219 - Municipal Map - CC approved on 04/06/2008 - SUP update of 05/04/2019

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

[Metadata] - 2015 Census Urban Areas and Urbanized Clusters with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016. The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/uac15.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

GIS and PDF data set of the Urban Planning Documents of the Local Urban Planning Plan of PIERRELATTE 26235 — PLU approved on 15/01/2013 Updated 10/10/2016 — FIN of VALIDITE: 11/08/2017 Updated

Introduction

The Cloud GIS market is on a trajectory of robust growth, projected to reach a value of USD 3,303.1 Million by 2033, from USD 891 Million in 2023, with a compound annual growth rate (CAGR) of 14% during the forecast period spanning from 2024 to 2033. Cloud GIS, a technology leveraging cloud computing to manage geographic information system (GIS) data, is witnessing this expansion due to various factors, including the rising demand for real-time data access, the scalability of cloud services, and ongoing digital transformation efforts across industries.

The Cloud Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing adoption of cloud technologies across various sectors. This growth can be attributed to several factors, including the scalability, flexibility, and cost-effectiveness of cloud-based solutions. These systems enable users to store, manage, and analyze geographical data without substantial investment in hardware infrastructure, making GIS tools accessible to a broader range of industries and organizations.

However, the market faces challenges, notably concerns regarding data security and privacy. As geographic data often includes sensitive information, the potential for data breaches makes some organizations hesitant to adopt cloud-based GIS solutions. Moreover, the reliance on continuous internet connectivity can pose operational challenges in regions with unstable internet services.

Despite these challenges, the Cloud GIS market presents substantial opportunities for new entrants. The ongoing digital transformation and the expanding need for location-based data across sectors like urban planning, environmental monitoring, and transportation logistics create a fertile ground for innovative solutions. New players can differentiate themselves by offering enhanced security features, customized solutions, and robust offline capabilities to address existing market gaps.

Basic information

This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see https://zenodo.org/records/10982836). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected:

1. s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00
2. s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00
3. s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00
4. s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00
5. s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00
6. s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00

For more detailed description of the experiments see the TURBAN project website at https://www.project-turban.eu/.

General organisation, variables and file nomenclature

Each selected epizode (s1-s6) has three subfolders; input files in ASCII (output-ascii) or GeoTiff (output-gis) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (output-png).

Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable vsrc_PM10 is the concentration of volume source emissions (VSRC) of the PM10, vsrc_PM25 is the concentration of PM2.5, vsrc_NO is the concentration of NO and vsrc_NO2 is the concentration of NO2.

Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019.

Emissions (see section 2.4.3 in Resler et al., 2024)

The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources.

Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP).

The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river.

Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024).

Acknowledgements

The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.

Literature

Note that some sources are available only in Czech language.

Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2023-2740

Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí znečišťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí znečišťujících látek na území aglomerace CZ01 Praha. Partially available at: https://www.atem.cz/neevidovane_zdroje.php

Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf

Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality.

EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources

van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf

EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: https://data.europa.eu/doi/10.2800/293657

OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en

LABOR MARKET ENGAGEMENT INDEXSummary

The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

Interpretation

Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

****Converted to NAD83(2011) by ProWest & Associates Inc in January of 2019."The year 2035 regional land use plan incorporates the basic principles and concepts of the previously adopted plans. The plan promotes a compact, centralized regional settlement pattern, with urban development recommended to occur within, and along the periphery of, existing urban centers; promotes the location of new urban development in areas which are physically suitable for such development and which may be readily served by basic urban services, including sanitary sewer, water supply, and public transit services; and seeks to preserve the environmentally sensitive lands and the most productive farmlands in the Region. Like the previous plans, the new plan is advisory in nature. Plan implementation will depend largely upon the willingness of county and local governments to use land use controls to shape development patterns in the regional interest. The year 2035 regional land use plan will provide a sound regional development framework needed in support of transportation and other public facility planning at the regional level, and in support of the preparation of comprehensive plans and related plan implementation efforts by county and local units of government in the Region. " http://www.sewrpc.org/SEWRPCFiles/Publications/pr/pr-048_regional_land_use_plan_for_se_wi_2035.pdf

All GIS and PDF data of the Urban Planning Documents of the Local Urban Planning Plan of PUYGIRON 26257 - PLU approved on 10/05/2012 enforceable on 25/06/2012 - END OF VALIDITY: 09/09/2016 SUP update