This project is just getting off the ground, but the intent is to start by mapping historic corner store buildings in our community. Identifying where historic corner stores are located, their current state of use and condition, and other useful information will begin to get a bigger picture of the history of our neighborhoods and the potential for these structures to catalyze hyper-local regeneration. What can the locations of these structures tell us about the neighborhoods they occupy?In some cases these storefronts have managed to hold on and provide the community glue, weathering the detrimental effects of big box stores and other factors that have gutted neighborhood stores. Is there an opportunity to celebrate and support the corner stores that still exist and can we find ways to prioritize rehabilitating the structures that could contribute to neighborhood revitalization? Could the historic corner store become the new cornerstones of neighborhoods?

Homelessness has been a consistent problem for the city of Louisville for decades now. Despite efforts from the city government and local nonprofits, homelessness increased 139% last year alone. The Covid-19 pandemic significantly worsened the crisis, but the risk factors that contribute to homelessness are still endemic across the city: lack of affordable housing, lack of access to physical and mental healthcare, stagnant wages, etc. Homelessness has negative effects on mortality, personal health of the homeless, and public health in general (also see here, no paywall). When I recently attended a strategy meeting for the Louisville Downtown Partnership, one of the top issues voted by attendees was the rise of homelessness downtown. This could come from genuine care or that many Americans associate homeless people with crime. Everyone benefits when the issues that cause homelessness are addressed effectively, and a vital part of that is knowing what areas are most at-risk.The app above was made to map certain risk factors across Jefferson County. The risk factors include percent of households with 50%+ income going to rent, persons without health insurance coverage, percent of households at or below the poverty line, percent of households using public assistance, percent of persons reporting extensive physical and mental distress, unemployment, along with other economic and health-based factors. This doesn’t include every possible factor that could cause homelessness, but many that have strong effects. A dummy census tract was made with all the worst possible outcomes for risk factors, which was then used to rank the similarity of every census tract in Jefferson County; the lower the rank, the more at-risk the tract is. The app allows you to click through every tract in the county and see the ten most at-risk ones.The most at-risk places tend to line up with the west end and areas of the city that were historically redlined. These areas also saw mass amounts of “urban renewal” in the 60s and 70s. They also tend to line up with areas of the city that face the highest eviction rates (thanks to Ryan Massey for pointing this out).

This layer presents the 2020 U.S. Census Tract boundaries of the United States in the 50 states and the District of Columbia. This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

Analysis condicted by ABR Inc.–Environmental Research & Services.Data from ADFG/BLM/NSB and ConocoPhillips Alaska Inc.For Brownian Bridge Movement Models - Conducted dynamic Brownian Bridge Movement Models (dBBMM) to delineate movmeents on seasonal herd ranges. dBBMM models were run using the move package for r using the following methods.1. Locations within 30 days of first collaring were removed from the analysis. 2. Selected females from PTT and GPS collars during the date range July 1 2012–June 30 2017 and individuals having more than 30 locations per season.3. ran a dBBMMM model for each individual during each season using 1 km pixels. 4. Calculate the 95% isopleth for each individual.5. Overlap all 95% isopleths and calculate the proportion of animals using (as defined by 95% isopleth) each pixel. Value shown is proportion times 1000. Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30). For Kernel Density Estimates - Conducted Kernel Density Estimation (KDE) to delineate seasonal herd ranges. Kernels were run using the ks package for r and the plugin bandwidth estimator. 1. Locations within 30 days of first collaring were removed from the analysis. 2. The mean latitiude and longitude for each animal was calculated for each day.3. A KDE utilization distribution was calculated for Julian day of the season (all years combined). 4. The daily KDE uds were averaged across the season. This method accounts for individual's movements during the seasons without the overfitting that results from using autocorrelated lcoations from individuals.Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30).

The IPUMS National Historical Geographic Information System (NHGIS) provides free online access to summary statistics and GIS files for U.S. censuses and other nationwide surveys from 1790 through the present. NHGIS boundary files are derived primarily from the U.S. Census Bureau's TIGER/Line files with numerous additions to represent historical (1790-1980) boundaries that do not appear in TIGER/Line files. For more recent boundary files (1990 or later), NHGIS typically makes only a few key changes to the TIGER/Line source: (1) we merge files that are available only for individual states or counties to produce new nationwide or statewide files, (2) we project the data into Esri's USA Contiguous Albers Equal Area Conic Projected Coordinate System, (3) add a GISJOIN attribute field, which supplies standard identifiers that correspond to the GISJOIN identifiers in NHGIS data tables, (4) we rename files to use the NHGIS naming style and geographic-level codes, (5) we add NHGIS-specific metadata, and (6) most substantially, we erase coastal water areas to produce polygons that terminate at the U.S. coasts and Great Lakes shores.NHGIS derived this shapefile from the U.S. Census Bureau's 2020 TIGER/Line Shapefiles.

Key Activity Centres as shown in the Land Use Recovery Plan.Key Activity Centres: Reference layer included in all RPS- UDS PC1 maps

Published in the notification of the decisions made by the Regional Council on the Commisioners' reccomendations to Proposed Plan Change 1 to Regional Policy Statement regarding the Urban Development Stratgey 19-Dec-2009.

http://ecan.govt.nz/our-responsibilities/regional-plans/rps/Pages/proposed-change-1-decisions.aspx

This dataset (JDD) is part of a data set that describes land use constraints related to anthropogenic risks, including, for each constraint, the government services or communities to be consulted. The initial target corresponds to those involved in the management of urban planning who wish to know these constraints in a given area of the Greater East region. In the context of the procedure “Treatment of requests relating to constraints of land use due to anthropogenic risks”, which oversees the consultations of the departmental units of DREAL and the Department of Anthropic Risk Prevention, the agricultural installations which present constraints in terms of operational planning are also identified. The “Agricultural Facilities” layer represents the municipalities in which at least one of these facilities is present. For each of these municipalities, it states: The name of the municipality The insee number of the municipality The name of the operator The S3IC number The address of the installation The scheme of the establishment The types of projects concerned The service of the State concerned How to obtain further information Description of the perimeter of the constraints Regulatory sources of the perimeter of constraints Sources of the data The data scale The internal contact SPRA The date of the initial state of play The Layer Update Dates The objective of the layer and to make it possible to identify areas subject to the constraints induced by these installations, to make available the information described above, and in particular to direct any consultations to the relevant interlocutors. The data is intended for all persons who may consult the UDs, SPRA and DDPPs on subjects related to anthropogenic risks, including the training services of local authorities, the other departments of the DREAL and the State, the notaries. Contact point: The agent of the Anthropic Risk Prevention Service (SPRA) in charge of operational planning. Name of the GIS data: MU_INSTALLATIONS_AGRICOLES_R44.shp

Data created 2018.An example UD map for a selected Special Protection Area (SPA) in which the species in question is listed as a feature. UDs are based upon the distribution of birds that originate from colonies found within the boundaries of the SPA (colonies defined as per the Seabird 2000 census). SPA shapefiles taken from JNCC (date accessed 01/08/2018). The example demonstrates how we can create UDs at different spatial scales for bespoke combinations of Seabird 2000 colonies.The full technical report describing the work is available here and the guidance notes for this data can be found here.

This Data Set (JDD) is part of a data set that describes land use constraints related to anthropogenic risks, including, for each constraint, the government services or communities to be consulted. The initial target corresponds to those involved in the management of urban planning who wish to know these constraints in a given area of the Greater East region. The accuracy of this JDD is the municipality. The JDD “Knowledge Mining Risks” represents municipalities on which constraints induced by mining risks have been transmitted to the Departmental Directorate of the Territories concerned, with a view to being brought to the attention of the mayors of the municipalities concerned. It states: • The department • The name of the municipality • The insee number of the municipality • Information enabling the site to be identified • Types of SDAs and projects affected by public utility easements • The service of the State concerned • The approach to obtain further information • Description of the perimeter of the constraints • Regulatory sources of the scope of constraints • Data sources • The scale of the data • The internal contact SPRA • The date of the initial state of play • Layer Update Dates The objective of the layer is to identify the constraints induced by known mining risks, to make available the information described above, and in particular to direct any consultations to the relevant interlocutors. DREAL does not need to be consulted on these constraints. The data is intended for all persons who may consult the UDs and the SPRA on subjects related to anthropogenic risks, including the local authorities’ teaching services, the other departments of DREAL and the State, the notaries. Contact point: The agent of the Anthropic Risk Prevention Service (SPRA) in charge of operational planning. Name of the GIS layer: MU_MINE_CONNU_DREAL_R44

This analysis uses location data collected on pronghorn antelope that were fitted with GPS collars in Idaho for 2003 – 2020. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). For pronghorn antelope, the anchor point used to measure distances from occurred near June1 since pronghorn antelope have a higher spatial fidelity to this time of year relative to more transient winter range locations. Fall and spring migration locations are used for the migration route analysis. After individual pronghorn antelope’s spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Single seasonal migrations are then rescaled to use only the upper 99 percent volume contour. In this process the 99 percent value contour is subtracted from the UD and resulting values less than 0 are rescaled to zero. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDs are combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by two individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The population level UDs is used to estimate seasonal migration stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers. Upper Snake River Plain Seasonal Migration StatisticsAnalyzed/Prepared by: Scott BergenDecember 2020Spatial MetricsAverage length of Migration: 83.8 milesMaximum Migration Length: 136.1 milesMinimum Migration Length: 30.5 milesTotal Migrations Analyzed: 51Total Number of Individuals: 33Total Number Spring Migrations: 25Total Number Fall Migrations: 26Of 51 individual seasonal migrations, 51 used Brownian bridge movement models.Temporal DataExtent of Study: October 5, 2004 – November 11, 2011Spring MigrationFall MigrationStart Date AverageMarch 24October 10 Minimum February 27September 18 MaximumMay 12November 5End Date AverageApril 25October 31 MinimumApril 2October 8 MaximumMay 29January 15Duration Average31.6 days20.4 days Minimum3 days3 days Maximum64 days97 daysMigration Use Class StatisticsUse ClassAcres 1 individual511,594 Low (>2 individuals)276,390 Medium (10-20%)114,266 High (>20%)166,030 Stopover104,072

This analysis uses location data collected on mule deer that were fitted with GPS collars in Idaho for 2003 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual mule deer spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a < 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers.

Data created 2018.An example UD map for a selected Special Protection Area (SPA) in which the species in question is listed as a feature. UDs are based upon the distribution of birds that originate from colonies found within the boundaries of the SPA (colonies defined as per the Seabird 2000 census). SPA shapefiles taken from JNCC (date accessed 01/08/2018). The example demonstrates how we can create UDs at different spatial scales for bespoke combinations of Seabird 2000 colonies.The full technical report describing the work is available here and the guidance notes for this data can be found here.

Data created 2018.UK-level utilisation distributions (UD) for European shag. UDs are provided in a sequence of 5% contours for each species starting at the 5% utilisation distribution and ending at the 95% utilisation distributions. Within the ecological literature the 95% UD is often used to define the 95% home range of a species and the 50% UD is used to define the 50% core range of a species.

Guidance notes for this data can be found here.

FQHC data mapped from the 2018 UDS data.

Map showing FQHCs, NAFCs, Hospitals, Public Health Departments, and Direct Relief partners. UDS data was collected for the map from the Health Resources and Services Adminstration.

Data created 2018.An example UD map for a selected Special Protection Area (SPA) in which the species in question is listed as a feature. UDs are based upon the distribution of birds that originate from colonies found within the boundaries of the SPA (colonies defined as per the Seabird 2000 census). SPA shapefiles taken from JNCC (date accessed 01/08/2018). The example demonstrates how we can create UDs at different spatial scales for bespoke combinations of Seabird 2000 colonies.The full technical report describing the work is available here and the guidance notes for this data can be found here.

Data created 2018.An example UD map for a selected Special Protection Area (SPA) in which the species in question is listed as a feature. UDs are based upon the distribution of birds that originate from colonies found within the boundaries of the SPA (colonies defined as per the Seabird 2000 census). SPA shapefiles taken from JNCC (date accessed 01/08/2018). The example demonstrates how we can create UDs at different spatial scales for bespoke combinations of Seabird 2000 colonies.The full technical report describing the work is available here and the guidance notes for this data can be found here.

Data created 2018.UK-level utilisation distributions (UD) for black-legged kittiwake. UDs are provided in a sequence of 5% contours for each species starting at the 5% utilisation distribution and ending at the 95% utilisation distributions. Within the ecological literature the 95% UD is often used to define the 95% home range of a species and the 50% UD is used to define the 50% core range of a species.

The full technical report describing the work is available here and the guidance notes for this data can be found here.

Data created 2018.UK-level utilisation distributions (UD) for European shag. UDs are provided in a sequence of 5% contours for each species starting at the 5% utilisation distribution and ending at the 95% utilisation distributions. Within the ecological literature the 95% UD is often used to define the 95% home range of a species and the 50% UD is used to define the 50% core range of a species.

The full technical report describing the work is available here and the guidance notes for this data can be found here.

Data created 2018.An example UD map for a selected Special Protection Area (SPA) in which the species in question is listed as a feature. UDs are based upon the distribution of birds that originate from colonies found within the boundaries of the SPA (colonies defined as per the Seabird 2000 census). SPA shapefiles taken from JNCC (date accessed 01/08/2018). The example demonstrates how we can create UDs at different spatial scales for bespoke combinations of Seabird 2000 colonies.The full technical report describing the work is available here and the guidance notes for this data can be found here.