The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.” Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety element of the evacuation routes from different jurisdictions as well as their use in various types of evacuation scenarios such as wildfire, flooding, or landslides. 2) Perform network analyses and modeling based on the team’s experience with road network performance, access restoration, and critical infrastructure modeling, for a set of case studies, as well as, assessing their performance considering the latest evacuation research. 3) Analyze how well current bus and rail routes align with evacuation routes; and for a series of case studies, using data from previous evacuations, evaluate how well aligned the safety elements of the emerging plans are, relative to previous evacuation routes. And 4) analyze different metrics about the performance of the evacuation routes for different segments of the population (e.g., elderly, mobility constrained, non-vehicle households, and disadvantaged communities). The database and assessments will help inform infrastructure investment decisions and to develop recommendations on how best to maintain State transportation assets and secure safe evacuation routes, as they will identify the road segments with the largest impact on the evacuation route/network performance. The project will deliver a GIS of the compiled plans, a report summarizing the creation of the database and the analyses and will make a final presentation of the study results. Methods The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22121490%2F7189944f8fc292a094c90daa799d08ca%2FChatGPT%20Image%2015%20Kas%202025%2014_07_37.png?generation=1763204959770660&alt=media" alt="">

🌆 About This Dataset

This synthetic dataset simulates 300 global cities across 6 major geographic regions, designed specifically for unsupervised machine learning and clustering analysis. It explores how economic status, environmental quality, infrastructure, and digital access shape urban lifestyles worldwide.

🎯 Perfect For:

• 📊 K-Means, DBSCAN, Agglomerative Clustering
• 🔬 PCA & t-SNE Dimensionality Reduction
• 🗺️ Geospatial Visualization (Plotly, Folium)
• 📈 Correlation Analysis & Feature Engineering
• 🎓 Educational Projects (Beginner to Intermediate)

📦 What's Inside?

🔥 Key Features

✅ Realistic Correlations: Income strongly predicts rent (+0.8), internet access (+0.7), and happiness (+0.6)
✅ Regional Diversity: Each region has distinct economic and environmental characteristics
✅ Clustering-Ready: Naturally separable into 4-5 lifestyle archetypes
✅ Beginner-Friendly: No data cleaning required, includes example code
✅ Documented: Comprehensive README with methodology and use cases

🚀 Quick Start Example

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Load and prepare
df = pd.read_csv('city_lifestyle_dataset.csv')
X = df.drop(['city_name', 'country'], axis=1)
X_scaled = StandardScaler().fit_transform(X)

# Cluster
kmeans = KMeans(n_clusters=5, random_state=42)
df['cluster'] = kmeans.fit_predict(X_scaled)

# Analyze
print(df.groupby('cluster').mean())

🎓 Learning Outcomes

After working with this dataset, you will be able to: 1. Apply K-Means, DBSCAN, and Hierarchical Clustering 2. Use PCA for dimensionality reduction and visualization 3. Interpret correlation matrices and feature relationships 4. Create geographic visualizations with cluster assignments 5. Profile and name discovered clusters based on characteristics

📚 Ideal For These Projects

• 🏆 Kaggle Competitions: Practice clustering techniques
• 📝 Academic Projects: Urban planning, sociology, environmental science
• 💼 Portfolio Work: Showcase ML skills to employers
• 🎓 Learning: Hands-on practice with unsupervised learning
• 🔬 Research: Urban lifestyle segmentation studies

🌍 Expected Clusters

🛠️ Technical Details

• Format: CSV (UTF-8)
• Size: ~300 rows × 10 columns
• Missing Values: 0%
• Data Types: 2 categorical, 8 numerical
• Target Variable: None (unsupervised)
• Correlation Strength: Pre-validated (r: 0.4 to 0.8)

📖 What Makes This Dataset Special?

Unlike random synthetic data, this dataset was carefully engineered with: - ✨ Realistic correlation structures based on urban research - 🌍 Regional characteristics matching real-world patterns - 🎯 Optimal cluster separability (validated via silhouette scores) - 📚 Comprehensive documentation and starter code

🏅 Use This Dataset If You Want To:

✓ Learn clustering without data cleaning hassles
✓ Practice PCA and dimensionality reduction
✓ Create beautiful geographic visualizations
✓ Understand feature correlation in real-world contexts
✓ Build a portfolio project with clear business insights

📊 Acknowledgments

This dataset was designed for educational purposes in machine learning and data science. While synthetic, it reflects real patterns observed in global urban development research.

Happy Clustering! 🎉

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

This dataset represents point locations of cities and towns in Arizona. The data contains point locations for incorporated cities, Census Designated Places and populated places. Several data sets were used as inputs to construct this data set. A subset of the Geographic Names Information System (GNIS) national dataset for the state of Arizona was used for the base location of most of the points. Polygon files of the Census Designated Places (CDP), from the U.S. Census Bureau and an incorporated city boundary database developed and maintained by the Arizona State Land Department were also used for reference during development. Every incorporated city is represented by a point, originally derived from GNIS. Some of these points were moved based on local knowledge of the GIS Analyst constructing the data set. Some of the CDP points were also moved and while most CDP's of the Census Bureau have one point location in this data set, some inconsistencies were allowed in order to facilitate the use of the data for mapping purposes. Population estimates were derived from data collected during the 2010 Census. During development, an additional attribute field was added to provide additional functionality to the users of this data. This field, named 'DEF_CAT', implies definition category, and will allow users to easily view, and create custom layers or datasets from this file. For example, new layers may created to include only incorporated cities (DEF_CAT = Incorporated), Census designated places (DEF_CAT = Incorporated OR DEF_CAT = CDP), or all cities that are neither CDP's or incorporated (DEF_CAT= Other). This data is current as of February 2012. At this time, there is no planned maintenance or update process for this dataset.This data is created to serve as base information for use in GIS systems for a variety of planning, reference, and analysis purposes. This data does not represent a legal record.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset of 230M+ verified locations, covering businesses, commercial properties, and public places across 5000+ industry categories. Our dataset enables retailers, investors, and GIS professionals to make data-driven decisions for business expansion, location intelligence, and geographic analysis.

With regular updates and continuous POI discovery, Xverum ensures your mapping and business location models have the latest data on business openings, closures, and geographic trends. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into geospatial analysis, market research, and navigation platforms.

🔥 Key Features:

📌 Comprehensive POI Coverage ✅ 230M+ global business & location data points, spanning 5000+ industry categories. ✅ Covers retail stores, corporate offices, hospitality venues, service providers & public spaces.

🌍 Geographic & Business Location Insights ✅ Latitude & longitude coordinates for accurate mapping & navigation. ✅ Country, state, city, and postal code classifications. ✅ Business status tracking – Open, temporarily closed, permanently closed.

🆕 Continuous Discovery & Regular Updates ✅ New business locations & POIs added continuously. ✅ Regular updates to reflect business openings, closures & relocations.

📊 Rich Business & Location Data ✅ Company name, industry classification & category insights. ✅ Contact details, including phone number & website (if available). ✅ Consumer review insights, including rating distribution (optional feature).

📍 Optimized for Business & Geographic Analysis ✅ Supports GIS, navigation systems & real estate site selection. ✅ Enhances location-based marketing & competitive analysis. ✅ Enables data-driven decision-making for business expansion & urban planning.

🔐 Bulk Data Delivery (NO API) ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured formats (.csv, .json, .xml) for seamless integration.

🏆 Primary Use Cases:

📈 Business Expansion & Market Research 🔹 Identify key business locations & competitors for strategic growth. 🔹 Assess market saturation & regional industry presence.

📊 Geographic Intelligence & Mapping Solutions 🔹 Enhance GIS platforms & navigation systems with precise POI data. 🔹 Support smart city & infrastructure planning with location insights.

🏪 Retail Site Selection & Consumer Insights 🔹 Analyze high-traffic locations for new store placements. 🔹 Understand customer behavior through business density & POI patterns.

🌍 Location-Based Advertising & Geospatial Analytics 🔹 Improve targeted marketing with location-based insights. 🔹 Leverage geographic data for precision advertising & customer segmentation.

💡 Why Choose Xverum’s POI Data? - 230M+ Verified POI Records – One of the largest & most structured business location datasets available. - Global Coverage – Spanning 249+ countries, covering all major business categories. - Regular Updates & New POI Discoveries – Ensuring accuracy. - Comprehensive Geographic & Business Data – Coordinates, industry classifications & category insights. - Bulk Dataset Delivery (NO API) – Direct access via S3 Bucket or cloud storage. - 100% GDPR & CCPA-Compliant – Ethically sourced & legally compliant.

Access Xverum’s 230M+ POI Data for business location intelligence, geographic analysis & market research. Request a free sample or contact us to customize your dataset today!

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

CAP’s Analyst Shopping Center dataset is the most comprehensive resource available for analyzing the Canadian shopping center landscape. Covering over 3,500 shopping centers across the country, this dataset provides a full horizontal and vertical view, enabling analysts, data scientists, solution providers, and application developers to gain unparalleled insights into market trends, tenant distribution, and operational efficiencies.

Comprehensive Data Coverage The Analyst Shopping Center dataset contains everything included in the Premium dataset, expanding to a total of 39 attributes. These attributes enable a deep dive into deriving key metrics and extracting valuable information about the shopping center ecosystem.

Advanced Geospatial Insights A key feature of this dataset is its multi-stage geocoding process, developed exclusively by CAP. This process ensures the most precise map points available, allowing for highly accurate spatial analysis. Whether for market assessments, location planning, or competitive analysis, this dataset provides geospatial precision that is unmatched.

Rich Developer & Ownership Details Understanding ownership and development trends is critical for investment and planning. This dataset includes detailed developer and owner information, covering aspects such as: Center Type (Operational, Proposed, or Redeveloped) Year Built & Remodeled Owner/Developer Profiles Operational Status & Redevelopment Plans

Geographic & Classification Variables The dataset also includes various geographic classification variables, offering deeper context for segmentation and regional analysis. These variables help professionals in: Identifying prime locations for expansion Analyzing the distribution of shopping centers across different regions Benchmarking against national and local trends

Enhanced Data for Decision-Making Other insightful elements of the dataset include Placekey integration, which ensures consistency in location-based analytics, and additional attributes that allow consultants, data scientists, and business strategists to make more informed decisions. With the CAP Analyst Shopping Center dataset, users gain a data-driven competitive edge, optimizing their ability to assess market opportunities, streamline operations, and drive strategic growth in the retail and commercial real estate sectors.

Xtract.io’s massive 3.5M+ POI database represents a transformative resource for advanced location intelligence across the United States and Canada. Data scientists, GIS professionals, big data analysts, market researchers, and strategic planners can leverage these comprehensive places data insights to develop sophisticated market strategies, conduct advanced spatial analyses, and gain a deep understanding of regional geographical landscapes.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape with comprehensive POI coverage.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive POI database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Comprehensive Location Data? At LocationsXYZ, we: -Deliver 3.5M+ POI data with 95% accuracy -Refresh places data every 30, 60, or 90 days to ensure the most recent information -Create on-demand comprehensive POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide multi-industry POI data and polygon data in multiple file formats

Unlock the Power of Places Data With our comprehensive location intelligence, you can: -Perform thorough market analyses across multiple industries -Identify the best locations for new stores using POI database insights -Gain insights into consumer behavior with places data -Achieve an edge with competitive intelligence using comprehensive coverage

LocationsXYZ has empowered businesses with geospatial insights and comprehensive location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge 3.5M+ POI database.

Overview and Inspiration

I wanted to make some geospatial visualizations to convey the current severity of COVID19 in different parts of the U.S..

I liked the NYTimes COVID dataset, but it was lacking information on county boundary shape data, population per county, new cases / deaths per day, and per capita calculations, and county demographics.

After a lot of work tracking down the different data sources I wanted and doing all of the data wrangling and joins in python, I wanted to open-source the final enriched data set in order to give others a head start in their COVID-19 related analytic, modeling, and visualization efforts.

This dataset is enriched with county shapes, county center point coordinates, 2019 census population estimates, county population densities, cases and deaths per capita, and calculated per day cases / deaths metrics. It contains daily data per county back to January, allowing for analyizng changes over time.

UPDATE: I have also included demographic information per county, including ages, races, and gender breakdown. This could help determine which counties are most susceptible to an outbreak.

How this data can be used

Geospatial analysis and visualization - Which counties are currently getting hit the hardest (per capita and totals)? - What patterns are there in the spread of the virus across counties? (network based spread simulations using county center lat / lons) -county population densities play a role in how quickly the virus spreads? -how does a specific county/state cases and deaths compare to other counties/states? Join with other county level datasets easily (with fips code column)

Content Details

See the column descriptions for more details on the dataset

Visualizations and Analysis Examples

COVID-19 U.S. Time-lapse: Confirmed Cases per County (per capita)

https://github.com/ringhilterra/enriched-covid19-data/blob/master/example_viz/covid-cases-final-04-06.gif?raw=true" alt="">-

Other Data Notes

• Please review nytimes README for detailed notes on Covid-19 data - https://github.com/nytimes/covid-19-data/
• The only update I made in regards to 'Geographic Exceptions', is that I took 'New York City' county provided in the Covid-19 data, which has all cases for 'for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) and replaced the missing FIPS for those rows with the 'New York County' fips code 36061. That way I could join to a geometry, and then I used the sum of those five boroughs population estimates for the 'New York City' estimate, which allowed me calculate 'per capita' metrics for 'New York City' entries in the Covid-19 dataset

Acknowledgements

• Special thanks to NYTimes for all of their hard work gathering and consolidating all of the U.S. COVID19 related data on daily basis. Their git repo https://github.com/nytimes/covid-19-data/
• Also, thanks to ykzeng for the county population density estimates: https://github.com/ykzeng/covid-19/tree/master/data-

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

This de-duped dataset is used by our customers for many purposes, primarily to understand which countries the people who visit specific locations (more accurately, the mobile devices carried by those people) - perhaps the locations that they own/operate, perhaps those owned/operated by their competitors, or visited by their customers - originated.

If, for instance, you operate a hotel brand and want to understand the top ten countries that visitors to your City came from; if/how that changes seasonally over time, and by type of location (perhaps higher end visitors are more likely to come from the UK or Germany versus France or Italy) - to help you build out your data models or marketing in those countries and/or to help tailor your product offers towards their needs.

This data can be useful as a way to understand, for instance, whether there are specific geographical areas you might consider putting a new location; where you might buy billboard ads, advertising the ‘local’ store; to build your own mobility data models to help better understand visitation into your own/your competitors premises, or test hypotheses around changes in visitation patterns over time.

The Intuizi Country Origin Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create a de-identified dataset of Encrypted ID visitation/mobility data.

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

Study Objective and Design: A change vector analysis (CVA) was used to determine land cover changes and identify tree species that are best for urban greening based on carbon sequestration and air pollution. The study assessed land cover change in Kitwe, Zambia, from 1990 to 2015. This study identified the most planted urban tree species along Kitwe's main roads and highways and evaluated typical urban tree species' pH, RWC, total chlorophyll, ascorbic acid, and biomass.Place and Length of Study: The urban trees in Kitwe, Zambia, make up the study population. The city of Kitwe is a thriving centre for mining and commercial activities and is situated in Zambia's Copperbelt Province. The investigation took place between 2018 and 2019.Methodology: The NDVI and BSI indices were created using spectral indices created from Landsat images of Kitwe taken in 1990 and 2015, respectively. The size and direction of the land cover were then determined using change vector analysis, and a district database of land cover changes was constructed using GIS. Urban trees from the built-up area were utilised to create an inventory of common urban tree species based on the land cover classification. The Anticipated Performance Index (API), which measures the suitability of tree species for improving air quality, and the Air Pollution Tolerance Index (APTI), which measures the suitability of tree species for urban greening, are two of the three assessment methods that were employed. In addition, above-ground biomass (AGB) was employed to quantify thecarbon sequestration contribution of the current urban forest.Results: The study discovered that between 1990 and 2015, mining activity and urban growth in Kitwe both contributed to changes in the area's land cover. While the central business district still exhibits a persistent presence as a result of the town's age, having sprung up before the 1990s with more expansions in the new areas, areas being monitored showed low and medium change intensity, mostly in the northeast of the district. In the currentinvestigation, there was a significant difference in the relative abundance of species (p = 0.05). In the study site, Mangifera indica (RA = 12.3%) and Delonix regia (RA = 15.9%) were the two most prevalent species. According to the study, eleven species were found, and each has accumulated carbon in a unique way throughout time depending on its allometry and age. These distinctions in physiological response (tolerance) to air pollution are noteworthy. Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia were all identified as suitable tree species.Conclusion: Over the past 25 years, more than 50% of the land cover has changed, with the majority of that change occurring in regions that are now classified as built-up areas. The majority of Kitwe's urban forests are found in the populated areas and are made up of a variety of ornamental trees that are frequently cultivated for their aesthetic value, attractiveness, and shade. According to the research, this mixture also includes opportunistic urban trees (invasive species) and fruit-bearing trees intermingled with native species. Overall, this study suggests the following species: For urban trees suited for greening programmes aimed at improving air quality and providing shade and beauty in green areas, residences, and sidewalks that have a low air pollution environment, consider Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia.

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: 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

This dataset provides detailed information about schools in California, including district names, school types, multilingual program availability, addresses, and administrative contacts. The dataset is ideal for geospatial analysis, educational research, and resource allocation studies.

Potential Use Cases

• Education Insights Analyze the distribution of public vs. private schools in California. Identify districts with high concentrations of multilingual programs. Study patterns in educational program types across urban and rural regions.

• Geospatial Studies Map the geographical distribution of schools by ZIP code or district. Highlight underserved areas or gaps in access to educational resources.

• Policy Research Provide insights for education policymakers on resource allocation. Identify trends or disparities in school operations.

• Public Resource Create a searchable directory for parents and educators to locate schools.

Source and Compliance Source: All data was collected from publicly available government resources and/or school directories. Privacy Compliance: To adhere to privacy standards and Kaggle's guidelines, fields containing personally identifiable information has removed.

River Surface Reflectance Database

A Geospatial Resource

By [source]

About this dataset

This dataset provides an invaluable collection of surface reflectance values across all rivers in the contiguous USA which are ~60 meters wide or more. The set contains level 1 collection 1 data for Landsat 5, 7 and 8 and is geo-referenced to river centerlines with network topology (NHDPlusV2). As a result, users can quickly conduct detailed geospatial analysis with this comprehensive dataset. For example, each record contains the NHDPlusV2 centerline ID as well as various surface reflectance values such as those of red, green, blue and near infrared bands. These bands present the median reflectance of pixels detected within each Landsat scene that lie inside each reach's boundaries. This information helps us better understand how different components of aquatic ecosystems vary across space - aiding research activities such as habitat assessment or species migration studies like never before!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand Data: Once you clicked on it, you will get an overview of all information regarding this dataset like number of observations, original source for the data, types of columns etc.. You should also read through columns descriptions to better understand what each column contains.

• Exploratory Analysis & Visualization: After understanding what data is present in this dataset it's a good idea to do some exploratory analysis and visualization using graphical tools such as pandas for Python or Tableau for example. This will help you see patterns or trends which are otherwise difficult to identify at first glance when looking at raw numbers or text description alone alone. While visualizing focus specifically on those variables which have maximum impact on overall performance i-e reachids, latitudes and longitudes of rivers center lines etcetera as they are more likely to contain more useful insights than rest!

• Modeling & Prediction: Now that you know basic information regarding your dataset, try and build different regression models like linear regression (for predicting lengths), Time Series (predicting band values) or any other model depending upon your requirements to gain further insight into our datasets so decisions can be quickly taken based upon factual evidence!

5 Reachid_ID & COMID_ID Files : In addition , there are two files namely Reachid_ID & COMID ID in this dataset , containing river COMIDs (validation numbers) combined with their corresponding IDs . These files enable users quickly identify what particular center line correspondes with which reach easily without having manually go through hundreds if not thousands records!

Research Ideas

• Using the median reflectance values of the different bands, it could be used to identify areas with high or low chlorophyll concentrations in a river, which can give an indication of the water quality and presence of aquatic life.
• Comparing surface reflectance across different rivers over time could help identify changes in land use that have impacted the adjacent tributary system’s condition over time.
• By analyzing the surface reflectance levels for specific versions, ecological assessments could be performed on a river to determine its health and potential management strategies needed for protecting against human threats such as pollutants, sedimentation etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: COMID_ID.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

ForestOwn_v1 is a 250-meter spatial resolution raster geospatial dataset of forest ownership of the conterminous United States (CONUS). The dataset was prepared by the Forest Inventory and Analysis (FIA) program, Northern Research Station, Forest Service, United States Department of Agriculture (USDA), and differentiates forest from non-forest land and water, public and private ownership, and the percent of private forest land in corporate ownership. The forest/non-forest land/water classification is derived from the USDA Forest Service's CONUS Forest/Nonforest dataset. Public and private land ownership class is derived from the Protected Areas Database of the United States, Version 1.1 (CBI Edition). Corporate ownership of private forest land is derived from the Forest Service's 2007 Resources Planning Act (RPA) dataset, summarized over the Environmental Protection Agency's Original Environmental Monitoring & Assessment Program (EMAP) grid 648 square kilometer hexagon dataset.The ForestOwn_v1 dataset is designed for conducting geospatial analyses and for producing cartographic products over regional to national geographic extents.A corresponding Research Map (RMAP) has been produced to cartographically portray this dataset.

Original metadata date was 02/09/2011. Minor metadata updates were made on 05/10/2013, 04/16/2014, 12/21/2016, and 02/06/2017. Additional minor metadata updates were made on 04/20/2023.

On 07/23/2020 a newer version of these data became available (Sass et al. 2020).

Dataset Description

This dataset contains ten million synthetically generated sales transactions from various geographic locations across the globe. It includes details on product sales, revenue, geographic coordinates, and other relevant features that can be used for analyzing geographic influences on product demand.

File Information:

• File Name: geographic_product_demand_dataset_10M.csv
• Number of Records: 10,000,000
• Size: Approximately 903 MB
• Columns: 11

Columns Description:

1. Location ID: A unique identifier for each location.
2. City: The city where the sales occurred.
3. State: The state where the sales occurred, if applicable.
4. Country: The country where the sales occurred.
5. Latitude: Latitude coordinates for the sales location.
6. Longitude: Longitude coordinates for the sales location.
7. Product ID: A unique identifier for each product.
8. Product Category: The category of the product (e.g., Tablet, Washing Machine).
9. Sales Volume: The number of units sold in the transaction.
10. Sales Revenue: The revenue generated from the sale.
11. Date: The date of the sales transaction (in YYYY-MM-DD format).

Usage

This dataset is designed for geospatial analysis of product demand, sales forecasting, and machine learning tasks. You can explore geographic patterns in consumer demand and analyze how product categories and sales revenues vary across different regions.

Example Use Cases:

• Sales Analysis: Explore how different regions vary in terms of demand for luxury goods versus essential goods.
• Geospatial Analysis: Visualize the geographic distribution of sales volumes and revenues.
• Time Series Analysis: Investigate how product demand changes over time and across different regions.
• Machine Learning: Build models to predict sales revenue based on geographic and product-related factors.

Data Preprocessing Tips:

• Convert the Date column to a datetime format before conducting temporal analysis.
• Use one-hot encoding for categorical variables like Product Category if applying machine learning models.
• Utilize latitude and longitude coordinates for geospatial visualizations.