Author: M Crampton, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 4, grade 8, high schoolResource type: lessonSubject topic(s): mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Acquire skills in data based mapping.
2. Recognize patterns of distribution.
3. Analyze patterns of distribution.
4. Construct and evaluate hypotheses of those distributions.
5. Explain the concept of regions.Summary: Students become cartographers in this introductory lesson as they learn how to map data. Students will generate hypotheses based on the patterns and seek additional data to test the hypotheses. The lesson assumes data on U.S. states, but data at a local, national or global scale may be used.

The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

This dataset contains geometry data for the countries of the world together with their names and country codes in various formats. The primary use case is choropleths, color-coded maps. The data can be read as a pandas DataFrame with geopandas and plotted with matplotlib. See the starter notebook for an example how to do it.

The data was created by Natural Earth. It is in public domain and free to use for any purpose at the time of this writing; you might want to check their Terms of Use.

Photo by KOBU Agency on Unsplash

Anyone who has taught GIS using Census Data knows it is an invaluable data set for showing students how to take data stored in a table and join it to boundary data to transform this data into something that can be visualised and analysed spatially. Joins are a core GIS skill and need to be learnt, as not every data set is going to come neatly packaged as a shapefile or feature layer with all the data you need stored within. I don't know how many times I taught students to download data as a table from Nomis, load it into a GIS and then join that table data to the appropriate boundary data so they could produce choropleth maps to do some visual analysis, but it was a lot! Once students had gotten the hang of joins using census data they'd often ask why this data doesn't exist as a prepackaged feature layer with all the data they wanted within it. Well good news, now a lot off it is and it's accessible through the Living Atlas! Don't get me wrong I fully understand the importance of teaching students how to perform joins but once you have this understanding if you can access data that already contains all the information you need then you should be taking advantage of it to save you time. So in this exercise I am going to show you how to load English and Welsh Census Data from the 2021 Census into the ArcGIS Map Viewer from the Living Atlas and produce some choropleth maps to use to perform visual analysis without having to perform a single join.

We recruited 414 college students to participate in the experiment. Through the experiment, we collected their visual data and arranged them according to different visual indicators. Then we process our data through qualitative and quantitative analysis to get the final result.

We propose map reading experiments to quantitatively evaluate the selection of hue ranges for sequential color schemes on choropleth maps. In these experiments, 60 sequential color schemes with six base hues and ten hue ranges were employed as experimental color schemes, and a total of 414 college students were invited to complete identification, comparison, and ranking tasks. Both controlled and real-map experiments were performed, each involving a web-based survey and an eye-tracking experiment. In the controlled experiments, the shapes of the map objects were relatively regular, and attribute data were randomized. In contrast, the shapes were complex in real-map experiments, and real data were employed. Our findings show that widely used color schemes with a hue range of 0º yield poor performance in all tasks; 15º hue ranges yield good performance in the comparison and ranking tasks but poor performance in the identification task. For large hue ranges of 120-360º, participants showed...

Context

I was looking for a dataset that will help me to map all the Major Indian Cities using Geopandas but I couldn't find non. This dataset help me to achieve what I was looking for. This data can be used for choropleth map, foilage map using Geopandas. There was only state value(lat & long), which I found in existing datasets. So I found this dataset.

Content

This contains all the Major Cities and their respective Latitude and Longitude Values along with the rounded-off population and the exact population

Acknowledgements

Thanks to Simple Maps for making all this data available in one place, you can find the original dataset here:- https://simplemaps.com/data/in-cities

Inspiration

You can use this dataset for plotting various features about the Major Indian Cities with the help of Geopandas.

The mapviews extension enhances CKAN by adding the capability to display data as interactive maps, including both regular maps and choropleth maps. By utilizing LeafletJS, which offers broad browser compatibility, the extension allows users to visualize datasets geographically. This enhances data exploration and understanding within the CKAN platform. Key Features: Regular and Choropleth Maps: Enables visualization of datasets on maps, offering both standard map views and choropleth maps that represent data variations across geographic regions. LeafletJS Integration: Leverages LeafletJS, a JavaScript library, to create interactive and responsive maps, ensuring compatibility with a wide range of web browsers (IE7+ and modern browsers). GeoJSON Support: Supports GeoJSON format for defining geographical boundaries and features, allowing integration with various GIS data sources. Data Linking: Provides a mechanism to link data from a tabular resource to geographical features in a GeoJSON resource, allowing for data-driven map visualizations. Interactive Filters: Allows filtering of data based on regions clicked on the map. URL Redirection: Can redirect to another page with filters set based on the region clicked, enhancing navigation within a CKAN instance to resources that relate to the region. Integration with CKAN: The extension integrates with CKAN by providing new Resource View types, navigablemap and choroplethmap. These views can be added to resources within CKAN datasets. The extension utilizes CKAN's plugin system, requiring activation via the ckan.plugins configuration setting, and makes use of the Resource View functionality. Benefits & Impact: The mapviews extension provides enhanced data visualization capabilities within CKAN, allowing users to explore and understand spatial data more effectively. The interactive maps, can help reveal patterns, trends, through geographic data. The filtering capabilities further promote data discovery and analysis, enabling the user to examine regional variations in that are represented within the data which may include social, economic, or environmental factors.

This dataset contains a comprehensive collection of geographic shapefiles representing the boundaries of countries and territories worldwide. The shapefiles define the outlines of each nation and are based on the most recent and accurate geographical data available. The dataset includes polygon geometries that accurately represent the territorial extent of each country, making it suitable for various geographical analyses, visualizations, and spatial applications.

Content: The dataset comprises shapefiles in the ESRI shapefile format (.shp) along with associated files (.shx, .dbf, etc.) that contain the attributes of each country, such as country names, ISO codes, and other relevant information. The polygons in the shapefiles correspond to the land boundaries of each nation, enabling precise mapping and spatial analysis.

Use Cases: This dataset can be utilized in a wide range of applications, including but not limited to:

• Creating choropleth maps to visualize and analyze various socio-economic indicators by country.
• Conducting spatial analysis to study population distribution, territorial areas, and geographic trends.
• Performing geopolitical research and country-level comparisons.
• Integrating with other datasets to enrich geographic analyses and insights.

Source: The shapefile data is sourced from reputable and authoritative geographic databases, ensuring its accuracy and reliability for diverse applications.

These are the results obtained from an empirical test looking at the communicative effectiveness between two types of two dimensional (2D) map formats (Choropleth maps, and Cartograms) of the Greater London area of the United Kingdom. Participants were interviewed and observed individually during the procedure. The results contain the recorded measurements of spatial accuracy, and the time taken for each participant to answers 3 test questions. A post-test qualitative reaction of each participants' preference between the two map types is recorded, along with their gender, age, visual impediments, and self-assessed map reading ability.

NYC Open Data data: Boundaries of Neighborhood Tabulation Areas as created by the NYC Department of City Planning using whole census tracts from the 2010 Census as building blocks. These aggregations of census tracts are subsets of New York City's 55 Public Use Microdata Areas (PUMAs)

reformatted to add NTA code as ferature.id for use with plotly choropleth

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

The Pedestrian Network (pednet) was created by the DAV team at the City of Toronto, and it is based on the sidewalk inventory from Transportation Services, Toronto road centrelines, and manual collection from aerial imagery. Pednet is integrated with centerline intersections, traffic signals, pedestrian crosswalks and crossovers, traffic signal data from Transportation Services as well as other City of Toronto datasets. Pednet was built using a variety of open source libraries such as NetworkX, Pandana, Quantum GIS, and Space syntax, as well as production mapping tools from ESRI’s ArcPro/ArcMap. The project source code can be found on DAV’s GitHub account here, which includes the semi-automated offsetting method from the Sidewalk Inventory and the analytical procedures undertaken. Pednet is a data model resembling a network graph (edges and nodes) weighted by linear distance. Shortest routes were calculated from every building centroid in the city to the nearest nth amenity at the maximum distance of 5000m. Walk times were calculated in the nearest minutes, using the prescribed 1.0m/per-second velocity used by Transportation services. Two separate versions of pednet were created in this iteration of the project: 1) using actual linear distances as network weights, and 2) where crosswalks were “extended” by 20% of their length to impose additional impedance to their distances and walk times. For every address within the City of Toronto, the walk times were calculated to various amenities like schools, libraries, hospitals, supermarkets, TTC stops and convenience stores see Section 3. Walk times were assigned to individual addresses as attributes. We then aggregated all these walk times to the census tract level and calculated the minimum, maximum, standard deviation, median, and average walk times. We used these aggregated values to both: 1) relate walkability measures to Statistics Canada Census data for socio-demographic analysis, as well as 2) the building footprints, pednet centerlines, and census tract area boundaries to be used in choropleth maps contained within the following sections.

This dataset is flattened and multicounty communities are unsplit by county lines. Flattened means that there are no overlaps; larger shapes like counties are punched out or clipped where smaller communities are contained within them. This allows for choropleth shading and other mapping techniques such as calculating unincorporated county land area. Multicounty cities like Houston are a single feature, undivided by counties. This layer is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnThis layer can also be accessed from the FEMA ArcGIS viewer online: https://fema.maps.arcgis.com/home/item.html?id=8dcf28fc5b97404bbd9d1bc6d3c9b3cfrnrnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

Analysis of ‘Malaria in Africa’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lydia70/malaria-in-africa on 29 August 2021.

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

Context

Africa, the world's second-largest continent, a continent with a wide array of vibrant cultures each with its own deep history, continent number 2 of largest population, and the continent is home to wonderful wildlife you can spot when you go on safari! Let's focus on Africa in this dataset.

Malaria is a common disease in Africa. The disease is transmitted to humans through infected mosquito bites. Although you can take preventive measures against malaria, it can be life-threatening. This dataset includes the malaria cases in African countries, the incidence at risk, and data on preventive treatments against malaria.

Content

This dataset includes data on all African countries from 2007 till 2017. Each country has a unique ISO-3 country code, and the dataset includes the latitude and longitude point of each country as well. The dataset includes the cases of malaria that have been reported in each country and each year, as well as data on preventive measures that have been taken to prevent malaria.

Acknowledgements

The data on the incidence of malaria, malaria cases reported, and preventive treatments against malaria have been retrieved from the world bank open data source.

Inspiration

Each country has a unique ISO-3 country code. You can use the ISO-3 code to create choropleth maps and in the geospatial analysis. In addition, the dataset includes latitude and longitude points for each country.

Drinking water safety and sanitation include a risk factor for malaria. Can improved drinking water facilities and preventive measures decrease the risk of malaria infection?

Check out my notebook submission, feel free to copy the kernel for your analysis: https://www.kaggle.com/lydia70/notebook-malaria-in-africa The notebook submission includes geospatial analysis with plotly.

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

In June 2022, Thailand legalized recreational cannabis. Currently, cannabis is now the most consumed drug. Cannabis usage can increase inflammatory responses in the respiratory tract. Sharing of cannabis waterpipes has been linked to increased tuberculosis risks. Using a national in-patient databank, we aimed to 1) describe the spatiotemporal correlation between cannabis-related and tuberculosis hospital admissions, and 2) compare the rate of subsequent pulmonary tuberculosis admission between those with prior admissions for cannabis-related causes and those without. Both admission types were aggregated to the number of admissions in monthly and provincial units. Temporal and spatial patterns were visualized using line plots and choropleth maps, respectively. A matched cohort analysis was conducted to compare the incidence density rate of subsequent tuberculosis admission and the hazard ratio. Throughout 2017–2022, we observed a gradual decline in tuberculosis admissions, in contrast to the increase in cannabis-related admissions. Both admissions shared a hotspot in Northeastern Thailand. Between matched cohorts of 6,773 in-patients, the incidence density rate per 100,000 person–years of subsequent tuberculosis admissions was 267.6 and 165.9 in in-patients with and without past cannabis-admission, respectively. After adjusting for covariates, we found that a cannabis-related admission history was associated with a hazard ratio of 1.48 (P = 0.268) for subsequent tuberculosis admission. Our findings failed to support the evidence that cannabis consumption increased pulmonary tuberculosis risk. Other study types are needed to further assess the association between cannabis consumption and pulmonary tuberculosis.

This dataset underlies a choropleth map of Boston area communities in which areas are shaded according to the percentage of the population that was foreign-born during each decade. The data was drawn from the US Census of Population, as well as the American Community Survey.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Additional file 1: Dataset used in this study.

Synthetic populations for regions of the World (SPW) | Alabama

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (usa_140002904_data_v_0_9.zip)

Derived data files

Validation and measures files