The Roads database includes an inventory of road assets (roadways, blocks, intersections, sidewalks, curbs) with a spatial representation and various attached information. Aggregate pavement-type road assets represent carriageways located in the public domain and which are part of the local or arterial road network. Aggregate pavements are represented by polygons that are aggregated by type of use. Among the information associated with a roadway-type object is the date of construction, the date of resurfacing, the date of survey, the date of survey, the materials of the pavement, the type of foundation, the presence of bicycle lane, use, etc. island-type road assets represent malls located in the public domain and which are juxtaposed to the local or arterial road network. The islands are represented by polygons that are differentiated by their configuration. Among the information associated with an island-type object is the date of construction, the date of survey, the materials of the block and the border, the presence of trees, the type of block, etc. intersection-type road assets represent the intersections of motorways located in the public domain and which are part of the local or arterial road network. Intersections are represented by polygons that are cut according to the number of traffic axes. Information associated with an intersecting object includes the construction date, resurfacing date, survey date, survey date, intersection materials, foundation type, bike lane presence, etc. sidewalk-type road assets represent sidewalks and curbs juxtaposed with roadways in the public domain that are part of the local or arterial road network. Sidewalks and curbs are represented by polygons differentiated by category and type. Among the information associated with a sidewalk-type object is the construction date, the survey date, the type of sidewalk and curb, the materials of the sidewalk, the border and the developed strip, the presence of trees, the presence of a projection, the presence of a bicycle path, the use, etc. zone-type road assets represent the regions located between other road assets and which do not not part of the local or arterial road network. The areas are represented by polygons. Among the information associated with a zone-type object is the type of zone, etc. The data is also available in separate sets on the portal to support several uses: - Roadway and intersection - Sidewalk and islet - Off-street zone - Sidewalk and block Warnings - The data released on road assets are those in the possession of the City's geomatics team and are not necessarily up to date throughout the country. - The data disseminated on road assets are provided for information purposes only and should not be used for the purposes of designing or carrying out works or for the location of assets.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Here are a few use cases for this project:

1. Traffic Flow Analysis: The "Intersection" model could be used to monitor and analyze traffic flow at busy intersections. The model can detect various vehicle types and provide data that aids in understanding traffic patterns, congestions, peak hours, and more.

2. City Transportation Planning: The ability to distinguish between various classes of vehicles makes "Intersection" valuable for city planning departments. They can use the data from this model to make more informed decisions relating to road design, public transportation infrastructure, and vehicular traffic regulation.

3. Autonomous Vehicle Development: "Intersection" can be a useful tool in developing smarter self-driving cars. The ability to correctly identify various vehicle types can inform decision-making algorithms used in autonomous vehicles, leading to safer and more efficient rides.

4. Vehicle Based Advertising: For advertising companies, the "Intersection" model can be used to assess the types of vehicles passing through a specific location. This data can guide strategies such as billboard placement or targeted advertisements, focusing on demographic profiles associated with certain vehicle types.

5. Crash Analytics and Insurance Risk Evaluation: Insurance companies could use the "Intersection" model to analyze accident-prone areas by identifying the types of vehicles typically involved. This information could assist in adjusting insurance premiums or identifying high-risk areas.

1. OverviewRoad intersections are crucial nodes in urban networks, where transportation lanes converge and socioeconomic activity is concentrated. While methods to identify road intersections using raster maps, satellite images, and trace data have been explored, challenges in accuracy and consistency remain.This paper proposes a method for identifying intersections based on OpenStreetMap data, which records networks at the lane level. Unlike geometric line intersections in OpenStreetMap, the identified intersections “summarize” parallel lanes and incorporate branch information, such as counts and orientations. The proposed method uses a vector-raster fusion approach to initially locate intersections, followed by hierarchical geometric configuration to improve accuracy and extract branch data.Experimental results show that the method effectively handles complex road networks in various cities, accurately identifying intersections and their branches. Experiments conducted on OpenStreetMap data from 7 cities yielded over 98% precision and 97% recall, outperforming the popular OSMnx tool. Additionally, lane synthesis at intersections achieved 99.43% precision and 98.34% recall. Urban characteristics can be quantitatively analyzed based on the identified road intersections. For instance, the proportion of four-way road intersections in New York is 52.6%, whereas in London, it is 9.6%, which may be attributed to the differing urban histories of these cities.2. Instructions for dataThis dataset contains road intersection data of seven cities (Berlin, Beijing, London, Nanjing, New York City, Rome, Shanghai) identified by our proposed method.The data of each city are stored in the folder named by its abbreviation.In each folder, there will be five *.shp files:(1) Roadnetwork (roads.shp)This layer stores the OSM road network with tag attributes.(2) Intersection points (cross-res.shp)This layer stores the location of the intersection point and the general characteristics of the road layout, including the number of intersecting roads and layout type. This provides the necessary location information for mapping and spatial analyses.(3) Related road lines (fullLine-res.shp/fail-fullLine.shp)The original road lines related to intersections extracted from the road networks were stored in this layer, preserving the inherent attributes of the original dataset. In addition, matching information, indicating whether the lines represent the same road, was stored as an attribute.(4) Synthesized road lines (simpleLine-res.shp)The geometries of this layer store synthesized road lines representing road orientations, whereas the attributes store acquired characteristics, including roadway configurations and match indices, which allow the synthesized road lines to be connected to the original roads from which they were derived. This connection is achieved through “pt_id” linked with cross-res.shp and “match_id” linked with fullLine-res.shp.3. Instructions for codesThis code repository is organized into eight folders and two files:(1) Folder: candidateIdentifyThis folder contains code related to the identification of candidate junctions or intersections from the input data. Each script plays a specific role in the overall process:bufferThin.py: Implements a thinning algorithm to generate a skeleton of buffered road geometry.candidateIdentify.py: The core script for identifying candidate points for road intersections in a spatial dataset.fastJunction.py: Provides an optimized method for detecting junctions quickly in large datasets by leveraging a fast hit-or-miss operation.junctionGeo.py: Handles the geometric processing of junction points, focusing on transforming raster cells into geographical positions.(2) Folder: compareEvaluationclipByregion.py: Clips spatial data to a specific region, which is useful for limiting analysis to a predefined geographic area. This is typically used to obtain intersections identified within the randomly selected regions.(3) Folder: geometricConfigurationTemplateMatchThis folder includes tools for matching geometric templates of road intersections:templateMatcher.py: The main module that matches geometric configurations of road intersections to the determined templates.utils_g.py: Provides utility functions that assist in geometric operations and template matching processes.(4) Folder: maxRadiusBufferThis folder focuses on the code of a greedy algorithm that produces the largest nonoverlapping buffers for a given collection of points.:maxRadBuffer.py: Core script for this algorithm.(5) Folder: intersectionDistanceAnalysisThis folder contains scripts for analyzing distances between intersection points for parameter determination:distanceAnalysis.py: Calculates the distances between identified intersections and analyze their distribution.(6) Folder: proportionDrawerThis folder contains scripts using R to produce pie charts in Figure 14.(7) Folder: refineCandidateThis folder contains a script that refines the previously identified intersection candidates.refineCandidate.py: This core script refines the set of candidate intersections by applying further geometric or statistical methods.(8) Folder: resultAnalysisThis folder is used for analyzing the identified results for insights of urban characteristics:angleAnalysis.py: Compute and analyze angles between road branches at intersections.4. Instructions on how to reproduce each reported findingsThe file 'instructions_on_how_to_reproduce_each_reported_findings.md' details the steps to reproduce the figures and the tables shown in the paper.

The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical investigations. The properties of short- and medium-range local order structures are responsible for the electronic, optical and transport properties of these systems. Here, we present the BELLO open source code, a post-processing script-tool created for the automatic analysis and extraction of structural characteristics of disordered and amorphous systems. BELLO is agnostic to the code that generated single configurations or trajectories, it provides an intuitive access through a graphical user interface (GUI), and it requires minimal computational resources. Its capabilities include the calculation of the order parameter , the folded structure identification, and statistical analysis tools such as atomic coordination number and pair/angle-distribution functions. The working principles of the code are described and tested on ab initio molecular dynamics trajectories of amorphous chalcogenides.

The code is also on the GitHub repository https://github.com/behnood-dianat/BELLO

Van Gogh: The Bedroom (1888) vs. The Tree Oil Painting

  Tool Mark & Brushstroke Intersection – AI Forensics





  🌍 Project Vision

This dataset is part of the Tree Oil Painting Global Verification Project.It focuses on forensic comparison between Vincent van Gogh’s The Bedroom (1888) and The Tree Oil Painting (1880s), analyzing tool mark intersections, brushstroke grooves, and orientation patterns.
The goal is to establish cross-era stylistic fingerprints through… See the full description on the dataset page: https://huggingface.co/datasets/HaruthaiAi/VanGogh_TheBedroom1888_vs_TreeOilPainting_ToolMarkIntersection_AIForensics.

The Roads database includes an inventory of road assets (roadways, blocks, intersections, sidewalks, curbs) with a spatial representation and various attached information. Aggregate pavement assets represent roadways located in public areas that are part of the local or arterial road network. Aggregate pavements are represented by polygons that are aggregated by type of use. Among the information associated with a pavement object is the construction date, resurfacing date, survey date, survey date, pavement materials, foundation type, bicycle lane presence, use, etc. Intersecting road assets represent intersections of roadways located in the public domain and that are part of the local or arterial road network. Intersections are represented by polygons that are cut according to the number of traffic axes. Among the information associated with an intersecting object is the date of construction, the date of resurfacing, the date of survey, the date of survey, the materials of the intersection, the type of foundation, the presence of a bicycle path, etc. The data is also available in separate sets on the portal to support several uses: - Street asset (complete database) - [Sidewalk and island] lot] (/city-of-montreal/roadway-sidewalk-island) - Off-street zone Warnings - The data released on road assets are those in the possession of the City's geomatics team and are not necessarily up to date throughout the country. - The data released on road assets is provided for information purposes only and should not be used for the purposes of designing or carrying out works or for the location of assets.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Roads database includes an inventory of road assets (roadways, blocks, intersections, sidewalks, curbs) with a spatial representation and various attached information. Aggregate pavement assets represent carriageways located in the public domain and that are part of the local or arterial road network. Aggregate pavements are represented by polygons that are aggregated by type of use. Among the information associated with a pavement object is the construction date, resurfacing date, survey date, survey date, pavement materials, foundation type, bicycle lane presence, use, etc. Intersecting road assets represent intersections of roadways located in the public domain and that are part of the local or arterial road network. Intersections are represented by polygons that are cut according to the number of traffic axes. Among the information associated with an intersecting object is the date of construction, the date of resurfacing, the date of survey, the date of survey, the materials of the intersection, the type of foundation, the presence of a bicycle path, etc. The data is also available in separate sets on the portal to support several uses: - Street asset (complete database) - [Sidewalk and island] lot] (/city-of-montreal/roadway-sidewalk-island) - Off-street zone Warnings - The data released on road assets are those in the possession of the City's geomatics team and are not necessarily up to date throughout the country. - The data released on road assets is provided for information purposes only and should not be used for the purposes of designing or carrying out works or for the location of assets.This third party metadata element was translated using an automated translation tool (Amazon Translate).

Increasing improvements in sensor technologies as well as machine learning methods allow an efficient collection, processing and analysis of the dynamic environment, which can be used for detection and tracking of traffic participants. Current datasets in this domain mostly present a single view, preventing high accurate pose estimations by occlusions. The integration of different, simultaneously acquired data allows to exploit and develop collaboration principles to increase the quality, reliability and integrity of the derived information. This work addresses this problem by providing a multi-view dataset, including 2D image information (videos) and 3D point clouds with labels of the traffic participants in the scene. The dataset was recorded during different weather and light conditions on several days at a large junction in Hanover, Germany. Paper Dataset teaser video: https://youtu.be/elwFdCu5IFo Dataset download path: https://data.uni-hannover.de/vault/ikg/busch/LUMPI/ Labeling process pipeline video: https://youtu.be/Ns6qsHsb06E Python-SDK: https://github.com/St3ff3nBusch/LUMPI-SDK-Python Labeling Tool/ C++ SDK: https://github.com/St3ff3nBusch/LUMPI-Labeling

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Intersection mapping with traffic control device.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The SACS study area is subdivided into 22 planning reaches (Figure 4 1) derived from three datasets and visual edits based on coastal geomorphology and professional judgment. Datasets include the following:- The Nature Conservancy Ecoregions—boundaries of areas that The Nature Conservancy has prioritized for conservation- State boundaries- Maximum inland limit of Category 5 storm surge inundation represented by the NOAA Sea, Lake, and Overland Surges from Hurricanes (SLOSH) modelThe GIS process to develop the Planning Reaches entailed the follow:The most landward extent of the SLOSH model was manually measured. Based on that measurement a single sided buffer was generated contiguous to the Coast for the AOR. The buffer was manually edited to include some areas that fell outside the buffer distance, specifically in Northern North Carolina and around Mobile Alabama. The Union tool was then used in ArcGIS desktop to overlay Ecoregions and State boundary files. Then the intersect tool was used to overlay the SLOSH buffer with the Union file. The result of the Intersect was then manually cut along the lines defined by the coastal geomorphology using lines defined in the “Manual_Edit_lines” feature. The resulting feature class was then provided with names based on the state two-digit acronym and a sequential number.

Wolbachia are wide-spread, endogenous α-Proteobacteria of arthropods and filarial nematodes. 15–75% of all insect species are infected with these endosymbionts that alter their host's reproduction to facilitate their spread. In recent years, many insect species infected with multiple Wolbachia strains have been identified. As the endosymbionts are not cultivable outside living cells, strain typing relies on molecular methods. A Multi Locus Sequence Typing (MLST) system was established for standardizing Wolbachia strain identification. However, MLST requires hosts to harbour individual and not multiple strains of supergroups without recombination. This study revisits the applicability of the current MLST protocols and introduces Allele Intersection Analysis (AIA) as a novel approach. AIA utilizes natural variations in infection patterns and allows correct strain assignment of MLST alleles in multiply infected host species without the need of artificial strain segregation. AIA identifies pairs of multiply infected individuals that share Wolbachia and differ in only one strain. In such pairs, the shared MLST sequences can be used to assign alleles to distinct strains. Furthermore, AIA is a powerful tool to detect recombination events. The underlying principle of AIA may easily be adopted for MLST approaches in other uncultivable bacterial genera that occur as multiple strain infections and the concept may find application in metagenomic high-throughput parallel sequencing projects.

In the forensic investigation of questioned documents, it is often very important to know the deposition order of ink traces from two different writing tools at their intersection on a paper. In the present work, intersections of inks from several writing tools were studied using optical techniques that are standardly applied for questioned documents examination in a forensic laboratory, and an accelerator-based Ion Beam Analysis (IBA) technique called Secondary Ion Mass Spectrometry using MeV ions (MeV SIMS) that is applied in an accelerator facility. MeV SIMS provides molecular information about the studied inks from writing tools, which is an added value and can be also applied for the determination of deposition order but was so far relatively rarely used in forensic studies. Aim of this paper is to compare performance of optical techniques and MeV SIMS for several combinations of intersecting lines. Cases were divided into those in which optical techniques can distinguish used inks and those which are optically completely indistinguishable. In the latter cases, we show that although mass spectra of used inks (from blue ballpoint pens) had extremely small differences, these in combination with advanced and most importantly objective multivariate algorithms could be very beneficial in resolving the deposition order at the intersection of optically indistinguishable inks. In general, MeV SIMS proved to be more efficient for oil-based inks while difficulties were encountered with water-based ones, similar to optical methods.

Intersection mapping with traffic control device.This third party metadata element was translated using an automated translation tool (Amazon Translate).

This file contains the location of all traffic lights managed by the City of Montreal. The file contains the reference number of the intersection where the light is located, the names of the two streets that form the intersection, and the geographic coordinates of the center point of the intersection.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Intersection of target datasets with reference datasets.

Overview:

Living in a rural county, I have often felt the isolation many Tennesseans are forced to face when it comes to accessing medical care. While my family's average drive time ranges from 30 minutes to over an hour to access healthcare, many Tennesseans living in more remote counties are forced drive several times farther.

The story map, "Standing Alone," follows three individuals who have each been differently affected by the disparity in rural Tennessee healthcare. Through their stories, I wanted to peel back the layers of the Tennessee healthcare crisis with geospatial analysis, highlighting underserved counties and advocating for healthcare reform. When it comes to healthcare, no one deserves to be standing alone.

Methods:

Map Showing Rural and Urban Areas: The “USA Urban Areas” and the “USA Counties” layers, both feature layers created by Esri, were added to the map from the Living Atlas. The USA Counties layered was filtered to only counties inside Tennessee. The Derive New Locations analysis tool was then used to find “USA Urban Areas” that intersect the filtered “USA Counties” layer, producing the “Tennessee Urban Areas” layer. Additionally, the Derive New Locations analysis tool was used to find “USA Counties” that do not intersect “USA Urban Areas,” creating the “Tennessee Rural Areas” layer. Custom pop-ups were formatted for the layers. Map Showing Life Expectancy per Tennessee County: The layer, “County Health Rankings 2021” by esri_demographics, was added from the Living Atlas and filtered to show only Tennessee counties. The layer was styled with “Counts and Amounts (Color)” style to show the average life expectancy in years for individuals living in each Tennessee county. The layer “Tennessee Urban Areas”, mentioned above, was also added to the map, and custom pop-ups were created for both layers. Map Showing Percent of Population Living Below the Poverty Level: The layer, “ACS Poverty Status Variables – Boundaries, created by Esri, was added from the Living Atlas and filtered to show only Tennessee counties. This layer was then joined with the Life Expectancy layer created for the Map Showing Life Expectancy per Tennessee county using the Join Features analysis tool, and the resulting layer was styled using “Counts and Amounts (Color)” style to show the percent of population whose income in the past 12 months is below the poverty level. Lastly, the “Tennessee Urban Areas” layer was added to the map, and custom pop-ups were configured for the layers. Map Showing Dr. Copeland’s Office and the Cumberland River Hospital: Addresses and labels for each location were added to an ArcGIS StoryMaps Express Map. Map Showing Rural Counties with Medically Underserved Populations: Using data from the Health Resources Administration’s Find MUA/P (Medically Underserved Area/Population) tool, data showing rural counties with medically underserved populations was inserted in a custom .csv layer and uploaded as a layer. This layer was joined to “USA Counties” using the Join Features analysis tool, and the resulting layer was styled using the “Location (Single symbol)” style. Custom pop-ups were also added to this layer. Maps Showing Ms. Crouch’s Search for Emergency Medical Services: These maps were created by inserting addresses or cities of each location into an ArcGIS StoryMaps Express Map. Map Showing Fentress County Ambulance Station: This map was created by inserting the address of Fentress County Ambulance Service and the location of each city into an ArcGIS StoryMaps Express Map. Map Showing Sum of Ambulance Units per County: Using data from the Tennessee Health Department, a custom .csv layer with the total number of ambulances per EMS station was created and uploaded as a layer. This layer was joined to the “USA Counties Layer” using the Join Features analysis tool, and the resulting layer was styled using the “Counts and Amounts (Size)” style to show the sum of ambulances in each county. Custom pop-ups were added for this layer. Map Showing Hospitals That Have Closed Since 2010: A custom .csv file was created using data from a Tennessee Healthcare Campaign report, and this data was uploaded as a layer showing the location of each hospital that has closed since 2010. The “Tennessee Urban Areas” layer and the “Tennessee Rural Areas” layer were also added to this map. Lastly, custom pop-ups were configured for these layers. Map Showing Drive Time Areas to Trauma Hospitals: Using data from the Tennessee Health Department, a custom .csv file was uploaded as a layer showing the locations of Tennessee trauma hospitals. A drive time buffer was created using the Create Drive-Time Areas analysis tool to map locations 15, 30, 45, and 60 minutes away from a trauma hospital. The “USA Counties” layer was added from the Living Atlas, and the Derive New Locations analysis tool was used to find locations over 60 minutes away from a trauma hospital. Finally, custom pop-ups were added to the layers. Map Showing COVID-19 Case Rate per Hundred Thousand for Each State: Using data from the Centers for Disease Control, a custom .csv file was created and uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool. The resulting layer was styled using the “Counts and Amounts (Color)” style to display the case rate per hundred thousand, and customized pop-ups were made for the layer. Map Showing COVID-19 Death Rate per Hundred Thousand for Each State: Using the same layer created in for the Map Showing COVID-19 Case Rate per Hundred Thousand for Each State, the layer was changed to show the death rate per hundred. Customized pop-ups were also added. Map Showing Percent of Deadly COVID-19 Cases in Tennessee: Using data from the Tennessee Health Department, a custom .csv was created, and the percentage of deadly COVID-19 was calculated. This file was uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool and styled using “Counts and Amounts (Color)”. Finally, customized pop-ups were added to the map. Map Showing Percent Difference Between National Vaccination Average and County Rates: Using the same data as the Map Showing Percent of Deadly COVID-19 Cases in Tennessee, a custom attribute was created to show the percent difference between county vaccination rates and the national average. The map was styled using the “Counts and Amounts (Color)”, and customized pop-ups were created for the map.

The following methods were used to create the graphics in this story map.

Thumbnail of Clay County: This thumbnail was created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Clay County. A screen shot of this map was uploaded to the story map as an image.Thumbnail of Fentress County: This thumbnail was also created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Fentress County. Finally, a screen shot of this map was uploaded to the story map as an image.

All remaining graphics were custom images created in Microsoft PowerPoint.

Sources and Acknowledgements:

This map was created for the 2022 ArcGIS Online Competition for US High Schools.

I would like to give special thanks to my geomentor and my parents, whose help and guidance were invaluable during the creation of this story map.All sources for information, data, and photographs are included as links throughout the story map.