JOBS PROXIMITY INDEXSummaryThe jobs proximity index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a CBSA, with larger employment centers weighted more heavily. Specifically, a gravity model is used, where the accessibility (Ai) of a given residential block- group is a summary description of the distance to all job locations, with the distance from any single job location positively weighted by the size of employment (job opportunities) at that location and inversely weighted by the labor supply (competition) to that location. More formally, the model has the following specification: Where i indexes a given residential block-group, and j indexes all n block groups within a CBSA. Distance, d, is measured as “as the crow flies” between block-groups i and j, with distances less than 1 mile set equal to 1. E represents the number of jobs in block-group j, and L is the number of workers in block-group j. The Longitudinal Employer-Household Dynamics (LEHD) has missing jobs data in all of Puerto Rico and a concentration of missing records in Massachusetts. InterpretationValues are percentile ranked with values ranging from 0 to 100. The higher the index value, the better the access to employment opportunities for residents in a neighborhood. Data Source: Longitudinal Employer-Household Dynamics (LEHD) data, 2017. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 8. To learn more about the Jobs Proximity Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

With the forecast increase in air traffic demand over the next decades, it is imperative to develop tools to provide traffic flow managers with the information required to support decision making. In particular, decision-support tools for traffic flow management should aid in limiting controller workload and complexity, while supporting increases in air traffic throughput. While many decision-support tools exist for short-term traffic planning, few have addressed the strategic needs for medium- and long-term planning for time horizons greater than 30 minutes. This paper seeks to address this gap through the introduction of 3D aircraft proximity maps that evaluate the future probability of presence of at least one or two aircraft at any given point of the airspace. Three types of proximity maps are presented: presence maps that indicate the local density of traffic; conflict maps that determine locations and probabilities of potential conflicts; and outliers maps that evaluate the probability of conflict due to aircraft not belonging to dominant traffic patterns. These maps provide traffic flow managers with information relating to the complexity and difficulty of managing an airspace. The intended purpose of the maps is to anticipate how aircraft flows will interact, and how outliers impact the dominant traffic flow for a given time period. This formulation is able to predict which "critical" regions may be subject to conflicts between aircraft, thereby requiring careful monitoring. These probabilities are computed using a generative aircraft flow model. Time-varying flow characteristics, such as geometrical configuration, speed, and probability density function of aircraft spatial distribution within the flow, are determined from archived Enhanced Traffic Management System data, using a tailored clustering algorithm. Aircraft not belonging to flows are identified as outliers.

Maps at the links below are for general information only. The information shown is to be considered accurate only to the date shown on each map. All of the maps found on this page are not parcel specific. The PDF maps can be viewed by using Adobe Acrobat zoom in/zoom out tools. All maps found on this page are downloadable. Larger sizes are available in print from the Development Center. Please call 253-864-4165 for pricing.

Raw imaging data for Tsue, Kania, et al (2024), "Oligonucleotide-mediated proximity-interactome mapping (O-MAP): A unified method for RNA-targeted microenvironment-mapping in situ."

In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. Green space is defined as Trees & Forest and Grass & Herbaceous. Water is shown as "-99999" in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Feature layer generated from running the Find Existing Locations solutions for 25_percent_obesity_rate.Expression 25_percent_obesity_rate within a distance of 1 Miles from Map Notes (Areas)

Scale reduction from source to target maps inevitably leads to conflicts of map symbols in cartography and geographic information systems (GIS). Displacement is one of the most important map generalization operators and it can be used to resolve the problems that arise from conflict among two or more map objects. In this paper, we propose a combined approach based on constraint Delaunay triangulation (CDT) skeleton and improved elastic beam algorithm for automated building displacement. In this approach, map data sets are first partitioned. Then the displacement operation is conducted in each partition as a cyclic and iterative process of conflict detection and resolution. In the iteration, the skeleton of the gap spaces is extracted using CDT. It then serves as an enhanced data model to detect conflicts and construct the proximity graph. Then, the proximity graph is adjusted using local grouping information. Under the action of forces derived from the detected conflicts, the proximity graph is deformed using the improved elastic beam algorithm. In this way, buildings are displaced to find an optimal compromise between related cartographic constraints. To validate this approach, two topographic map data sets (i.e., urban and suburban areas) were tested. The results were reasonable with respect to each constraint when the density of the map was not extremely high. In summary, the improvements include (1) an automated parameter-setting method for elastic beams, (2) explicit enforcement regarding the positional accuracy constraint, added by introducing drag forces, (3) preservation of local building groups through displacement over an adjusted proximity graph, and (4) an iterative strategy that is more likely to resolve the proximity conflicts than the one used in the existing elastic beam algorithm.

This map illustrates Fort Worth buildings within walking distance to city parks and highlights strategic growth centers. Buildings are categorized by proximity: high (0.25 mi), medium (0.5 mi), and low (0.75 mi) access to parks. Growth centers such as Downtown, the Cultural District, Near Southside Medical District, and Near Southeast are concentrated near green spaces and major corridors, reinforcing the city’s focus on walkability and mixed-use development. The spatial pattern indicates that future growth will prioritize areas adjacent to parks to enhance livability and urban density. Recent census data supports this trajectory, showing population increases in these core areas, which aligns with planned development and infrastructure improvements.

Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. This accessibility is computed using a cost-distance algorithm which computes the “cost” of traveling between two locations on a regular raster grid. Generally this cost is measured in units of time.The input GIS data and a description of the underlying model that were developed by Andrew Nelson in the GEM (Global Environment Monitoring) unit in collaboration with the World Bank’s Development Research Group between October 2007 and May 2008. The pixel values representing minutes of travel time. Available dataset: Joint Research Centre - Land Resource Management Unit

This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

For every address in the City of Kitchener, a GIS spatial join has been created to select the closest Park, Playground, Elementary School, etc

Connecticut and Vicinity Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

We isolated nuclear mRNPs from yeast using an endogenous bi-molecular tagging approach on Sub2 and Hpr1.

This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. For specific information about methods used to develop data for each community, consult their individual metadata records: Austin, TX (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B6D2657C3-52F8-4E41-BC35-00EE9DAADE4E%7D); Birmingham, AL (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7Be2f2445f-f7df-4b83-9004-eb4e1c45bed9%7D); Baltimore, MD (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B920a6fb4-812e-421d-a4fa-f6a328c1f3d3%7D); Brownsville, TX (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7Bc31dcddc-6750-4504-9121-668f2c6d53d3%7D); Cleveland, OH (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B0ced9d35-9f11-46ca-abf0-1e10ebae1331%7D); Des Moines, IA (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B7BDA7EB9-C5F8-4C15-90A4-A3CB285671D9%7D); Durham, NC (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B38956084-2AB2-422C-B56D-068C8CA6AAFE%7D); Fresno, CA (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B5D957C70-5A37-48A6-B7D4-1E304710417F%7D); Green Bay, WI (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B8675EC55-2CCE-4B83-972A-A23CD6618B09%7D); Memphis, TN (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BBBF52A70-DCCF-4DB6-ACDE-82A321A03F8C%7D); Minneapolis/St. Paul, MN (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B6a46d7c8-a344-4d19-a111-0c96a2fda15f%7D); Milwaukee, WI (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BE0EC8E26-0688-4EC5-8611-BF300054820E%7D); New Bedford, MA (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B15DE4903-7C9D-4F61-9761-53D9904447C1%7D); New Haven, CT (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B9d0bac80-69fc-4649-9131-95f276da0ca0%7D); New York, NY (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B4860A2BF-B3EB-4199-A3F2-6DDB7E97F880%7D); Phoenix, AZ (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B42936F73-D645-4C86-820A-72A19FBB213A%7D); Pittsburgh, PA (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BA62B08BE-36D7-43EF-8C89-4EDC2EB5EF54%7D); Portland, ME (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BF3BA2EEB-784B-45C2-B8BD-DC2C2CEE2479%7D); Paterson, NJ (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B90E93159-7A1E-4BAB-95FC-8B04A15070E3%7D); Portland, OR (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B4671F2A5-B691-47F9-9A1D-C11AA0F952D0%7D); Salt Lake City, UT (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B4671F2A5-B691-47F9-9A1D-C11AA0F952D0%7D);Tampa, FL (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BC17799CA-F743-4CE6-BC71-3D4C82E5F2BC%7D); and Woodbine, IA (https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B667D6539-13C6-45A2-BD5B-80108BAA5213%7D). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

JOBS PROXIMITY INDEXSummaryThe jobs proximity index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a CBSA, with larger employment centers weighted more heavily. Specifically, a gravity model is used, where the accessibility (Ai) of a given residential block- group is a summary description of the distance to all job locations, with the distance from any single job location positively weighted by the size of employment (job opportunities) at that location and inversely weighted by the labor supply (competition) to that location. More formally, the model has the following specification: Where i indexes a given residential block-group, and j indexes all n block groups within a CBSA. Distance, d, is measured as “as the crow flies” between block-groups i and j, with distances less than 1 mile set equal to 1. E represents the number of jobs in block-group j, and L is the number of workers in block-group j. The Longitudinal Employer-Household Dynamics (LEHD) has missing jobs data in all of Puerto Rico and a concentration of missing records in Massachusetts. InterpretationValues are percentile ranked with values ranging from 0 to 100. The higher the index value, the better the access to employment opportunities for residents in a neighborhood. Data Source: ACS 2017 - 2021 5 year summary data. Longitudinal Employer-Household Dynamics (LEHD) data, 2017. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 8. To learn more about the Jobs Proximity Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2017 - 2021 ACSDate Updated: 10/2023

Additional file 1: Figure S1. Pearson correlation coefficients between the gene co-expression matrix and three different matrices based on spatial positioning of genes: the CGP map (blue bars), the raw gene proximity map (green bars), and the normalized gene proximity map (yellow bars) for each of the 23 chromosomes for 10 ENCODE cell lines. Figure S2. (A) ROC curve for the gene compartment classification using leading eigenvectors of the CGP matrix for GM12878 and K562 cell lines. The horizontal axis is the false positive rate (1 − specificity) and the vertical axis is the true positive rate (sensitivity). The red dot indicates the optimal operating point. Components of the top 50 leading eigenvectors were used as features for the classification model. (B) Effect of the number of eigenvectors used in the gene compartment label classifier. The horizontal axis represents the number of eigenvectors in the CGP matrix used for model construction, ranged from 1 to 50. The vertical axis is the average AUROC of the resultant model over the 10-fold cross validation. The red circles and blue squares (almost completely coincide) represent the GM12878 and K562 cell lines respectively. Using the first leading eigenvector alone does not yield a good classification result. By additionally incorporating the second and third eigenvectors, the AUROC witnesses a dramatic increase (from 0.57 to 0.70). On the other hand, using more than 10 eigenvectors does not provide a substantial performance improvement any more. Figure S3. Objective function based on the empirical gene expression profile and randomized profiles, computed using the raw gene proximity map. The histogram for randomized profiles is normalized to have zero mean. A main difference between the plots generated from the CGP and the raw gene proximity map is that for cell lines RPMI-7951, SJCRH30 and SK-N-DZ, the value of the gene proximity map-based objective function generated from the empirical expression profile is mixed with the values generated from randomized profiles. Figure S4. Change in relative spatial positioning of chromosomes between cell lines GM12878 and K562. The layout of this network is in the same way as Figure 6 in the main text, but the inter-chromosomal proximity matrix here was computed using the gene proximity map instead of the corrected proximity measure. As compared to Figure 6, the connections between chromosomes 3 and 10, and between chromosomes 9 and 22, are no longer easily identified. Table S1. Top 20 inter-chromosomal gene interactions in cell lines GM12878 and K562 respectively. These pairs of genes were selected based on the fact that they are located on different chromosomes and have the largest values in the corresponding CGP map.

This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This map was created in partnership with Friends of Ada Hayden and the City of Ames. The map is intended to help patrons identify running routes and distances.