This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

• Spatial join of precintvotingdata in Counties *Change the symbology for quantities of normalization yes_votes.

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.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.

This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.

Data processed on 24/04/2020 for assignment 4 of the Coursera ArcGis fundamentals. Spatial join used to join the voting data on a counties level. Adjusted the scale, implemented different map items and made the map ready to be exported.

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters. The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. 1.) Identify local maxima candidates. Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates. 2.) Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. 3.) Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with the following differences: - Different years and slightly different post-processing steps for InfoUSA data - Video study covers 5-county region (Imperial county not included) - Limited to 1km scale subcenters - Due to these differences, the final map of subcenters is different. A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The final step involved qualitatively comparing results at each scale to create the final map of 69 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas.

This is a collaboration between City of Los Angeles Mayor's Office, StreetsLA, and USC. To consolidate / aggregate many datasets for Street Sweeping. Task 2: to perform spatial join between Centerlines and Biweekly Posted Routes.

Vermont E911 Site locations (ESITEs) including buildings, facilities, and development sites; locations are represented by points. Points are attributed with addresses--composing an address points layer. Dataset is updated weekly.Field Descriptions:OBJECTID: Internal feature number. Auto-generated by Esri software.SEGMENTID: Unique segment ID.ESITEID: Unique ESITE ID.GEONAMEID: Ties ESITE to GEONAMEID (unique ID for each road name) in VT E911 Road Centerlines.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East etc.PRIMARYNAME: A concatenation of the street-name parts (PD, PT, SN, ST, SD).ALIAS1: Alternate road name.ALIAS2: Alternate road name.ALIAS3: Alternate road name.ALIAS4: Alternate road name.ALIAS5: Alternate road name.PRIMARYADDRESS: A concatenation of house number and street-name parts (PD, PT, SN, ST, SD).SITETYPE: Type of site. Uses SiteTypes domain*.TOWNNAME: Town name.MCODE: Municpal code.ESN: Emergency Service Number. Developed for each town that indicates a unique town code for each law, fire, and EMS provider. These providers are compared against the master list to determine if they are already present. If they are, the existing state code is used. If the provider is new, they are added to the state master list with the next unique provider number.ZIP: Zip code.PARCELNUM: Parcel number.GPSX: GPS X coordinate.GPSY: GPS Y coordinate.MAPYEAR: Date added to E911 data.UPDATEDATE: Update date.STATE: US State.FIPS8: Federal information processing standards codes.SPAN: Pulled from the VCGI parcel dataset via spatial join 1-3 times per year; NOT MAINTAINED DAILY.SUBTYPE: Field not in use.GlobalID_1: System-generated ID.UNITCOUNT: For commercial and residential, number of units in the site.PRIMARYADD1: Concatenation of house number, full street name, and E911 town. E911 TOWN (AKA E911 JBOUND) IS NOT ALWAYS THE SAME AS POSTAL TOWN NOR IS IT ALWAYS THE SAME AS TOWN DEFINED BY MUNICIPAL BOUNDARY. E911 TOWN (E911 JBOUND) was originally defined for the Master Street Address Guide (MSAG) Community; E911 JBOUND contains names chosen by towns for representing town names for 911 purposes.PRIMARYADD2: Concatenation of PRIMARYADD1 plus zip code.SITETYPE_MULTI1: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI2: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI3: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI4: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI5: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.COUNTY: County.COUNTRY: Country.SOURCEOFDATA: Source of data.DRIVEWAYID: Field not in use.ESZ: Emergency Service Zone--a defined area covered by four primary-response agencies.HOUSE_NUMBER: House number.HOUSE_NUMBERSUFFIX: For addresses not in compliance with standards (typically in urbanized areas where otherwise renumbering needs to occur). For example, a new house between 8 and 10 is built and the town calls it 8 1/2 or 8A instead of renumbering; the 1/2 or A would be in this field; there are approximately less than 300-400 of these cases.HOUSE_NUMBERPREFIX: For the three streets where alpha characters come before the house number (e.g., A20 or B12).FIPS: County FIPS number.Shape: Feature geometry.*SiteTypes Domain:ABANDONEDACCESS POINTACCESSORY BUILDINGAIR SUPPORT / MAINTENANCE FACILITYAIR TRAFFIC CONTROL CENTER / COMMAND CENTERAIRPORT TERMINALAMBULANCE SERVICEAUDITORIUM / CONCERT HALL / THEATER / OPERA HOUSEBANKBOAT RAMP / DOCKBORDER CROSSINGBORDER PATROLBUS STATION / DISPATCH FACILITYCAMPCAMPGROUNDCEMETERYCITY / TOWN HALLCOAST GUARDCOLLEGE / UNIVERSITYCOMMERCIALCOMMERCIAL CONSTRUCTION SERVICECOMMERCIAL FARMCOMMERCIAL GARAGECOMMERCIAL W/RESIDENCECOMMUNICATION BOXCOMMUNICATION TOWERCOMMUNITY / RECREATION FACILITYCOURT HOUSECULTURALCUSTOMS SERVICEDAY CARE FACILITYDEVELOPMENT SITEEBS TOWEREDUCATIONALEMERGENCY PHONE / CALLBOXFAIR / EXHIBITION/ RODEO GROUNDSFERRY TERMINAL / DISPATCH FACILITYFIRE STATIONFISH FARM / HATCHERYFITNESS FACILITYFOOD DISTRIBUTION CENTERGAS STATIONGATED W/BUILDINGGATED W/O BUILDINGGOLF COURSEGOVERNMENTGRAVEL PITGREENHOUSE / NURSERYGROCERY STOREHARBOR / MARINAHAZARDOUS MATERIALS FACILITYHAZARDOUS STORAGE FACILITYHEALTH CLINICHELIPAD / HELIPORT / HELISPOTHISTORIC SITE / POINT OF INTERESTHOSPITAL / MEDICAL CENTERHOUSE OF WORSHIPHYDROELECTRIC FACILITYICE ARENAINDUSTRIALINSTITUTIONAL RESIDENCE / DORM / BARRACKSLANDFILLLAW ENFORCEMENTLIBRARYLODGINGLOOKOUT TOWERLUMBER MILL / SAW MILLMANUFACTURING FACILITYMINEMOBILE HOMEMORGUEMULTI-FAMILY DWELLINGMUSEUMNATIONAL GUARD / ARMORYNUCLEAR FACILITYNURSING HOME / LONG TERM CAREOFFICE BUILDINGOFFICE OF EMERGENCY MANAGEMENTOIL / GAS FACILITYOTHEROTHER COMMERCIALOTHER RESIDENTIALOUTPATIENT CLINICPARK AND RIDE / COMMUTER LOTPHARMACYPICNIC AREAPOST OFFICEPRISON / CORRECTIONAL FACILITYPRIVATE AND EXPRESS SHIPPING FACILITYPSAPPUBLIC BEACHPUBLIC GATHERINGPUBLIC TELEPHONEPUBLIC WATER SUPPLY INTAKEPUBLIC WATER SUPPLY WELLPUMP STATIONRACE TRACK / DRAGSTRIPRADIO / TV BROADCAST FACILITYRAILROAD STATIONRESIDENTIAL FARMREST STOP / ROADSIDE PARKRESTAURANTRETAIL FACILITYRV HOOKUPSCHOOLSEASONAL HOMESINGLE FAMILY DWELLINGSKI AREA / ALPINE RESORTSOLAR FACILITYSPORTS ARENA / STADIUMSTATE CAPITOLSTATE GARAGESTATE GOVERNMENT FACILITYSTATE PARKSTORAGE UNITSSUBSTATIONSUGARHOUSETEMPORARY STRUCTURETOWN GARAGETOWN OFFICETRAILHEADTRANSFER STATIONUNKNOWNUS FOREST FACILITYUS GOVERNMENT FACILITYUTILITYUTILITY POLE W/PHONEVETERINARY HOSPITAL / CLINICVISITOR / INFORMATION CENTERWAREHOUSEWASTE / BIOMASS FACILITYWASTEWATER TREATMENT PLANTWATER TANKWATER TOWERWIND FACILITY / WIND TOWERYOUTH CAMP

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

This project aims to identify areas in Los Angeles that are at high risk of crime in the future and to propose optimal locations for new police stations in those areas. By applying machine learning to post-COVID-19 crime data and various socioeconomic indicators, we predict crime risk at the ZIP Code level. Using a location-allocation model, we then determine suitable locations for new police stations to improve coverage of high-risk zones. The results of our analysis can support the efficient allocation of public safety resources in response to growing demand and budget constraints, helping city officials optimize law enforcement services. The content of the archive- Jupyter Notebook- Data (GeoJSON, CSV)- Summary report PDF FileThe platform on which the notebook should be run.This notebook is designed to run on Datahub.Project materials - Project Material we created on AGOL 1 Los Angeles Crime Hotspothttps://ucsdonline.maps.arcgis.com/home/item.html?id=4bddbae65c164f2d9b0285e09cb2820e 2 Choropleth Map of Predicted Crime Levels by ZIP Codehttps://ucsdonline.maps.arcgis.com/home/item.html?id=e47abb448f0a411ab77c6ac754ba0c34 3. Optimizing LA Police Station: A Location Allocation Analysishttps://ucsdonline.maps.arcgis.com/home/item.html?id=2409da85c3fe410e9578a0eaaed8471e - ArcGIS StoryMaphttps://ucsdonline.maps.arcgis.com/home/item.html?id=cfbd4fc27a3b400296e4e31555951d27 Software dependencies - pandas: Used for loading, formatting, and performing matrix operations on tabular data.- geopandas: Used for loading and processing spatial data, including spatial joins and coordinate transformations.- shapely.geometry.Point: Used to create spatial point objects from latitude and longitude coordinates.- arcgis.gis, arcgis.features, arcgis.geometry, arcgis.geoenrichment: Used to retrieve and manipulate geographic data from ArcGIS Online and to extract population statistics using the GeoEnrichment module.- numpy: Used for feature matrix formatting and numerical computations prior to model training.- IPython.display (display, Markdown, Image): Used to format and display Markdown text, data tables, and images within Jupyter Notebooks.- scikit-learn: Used for building and evaluating machine learning models. Specifically, it was used for data preprocessing (StandardScaler), splitting data (train_test_split), model selection and tuning (GridSearchCV, cross_val_score), training various regressors (e.g.,LinearRegression, RandomForestRegressor, KNeighborsRegressor), and assessing performance using metrics such as R², RMSE, and MAE.Other Components we used - ArcGIS Online: Used to create and host interactive web maps for spatial visualization and public presentation purposes.- Flourish: Used to create interactive graphs and charts for visualizing trends and supporting the analysis.

Connecticut Erosion Susceptibility a 1:24,000-scale, polygon feature-based layer that was developed as a predictive tool to show areas most susceptible to terrace escarpment type erosion. The layer compiled from the soils and quaternary geology data layers and was field tested during October-December, 2005. The Erosion Susceptilibity layer was developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer does not represent eroding conditions at any one particular point in time, but rather base or general conditions which can be accounted for during planning or management strategies. The layer includes 4 types of areas susceptible to erosion, ranked 1 (most susceptible) through 4, and their descriptive attribute. Areas outside of the mapped polygons can be considered less susceptible to erosion. Data is compiled at 1:24,000 scale. This data is not updated.

Connecticut Erosion Sites is a site specific, point feature-based layer developed at 1:24,000-scale that includes decriptive information regarding the character of the erosion (severity, slope, geologic factors) at selected locations through out the state. The layer is based on information collected and compiled during October-December, 2005 while field testing the applicability of the Erosion Susceptilibity layer developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer represents conditions at a particular point in time. The layer includes 83 locations and descriptive attributes (site name, severity of erosion, description, etc) as well as attributes from a spatial join with merged soils and quaternary geology layers. Features are point locations that represent the selected study areas within the state; it is NOT a comprehensive inventory of erosion locations. Data is compiled at 1:24,000 scale. This data is not updated.

This is a collaboration between City of Los Angeles Mayor's Office, StreetsLA, and USC. To consolidate / aggregate many datasets for Street Sweeping. Task 2: to perform spatial join between Centerlines and Tracts in order to get the Median HHI from 2018 Demographics.

Title of reference article:Nine Days of Naptown Arrests: How and Why Spatial Data Should Discomfort UsAuthor J. Kevin ByrneDate authored: August 23, 2020Abstract: During nine successive days in 2019 Indianapolis (IN) police made arrests across six districts. Exploratory spatial data analysis (ESDA) revealed how variables of arrests, race, aggressive use of force (UOF), injuries, and their location interact with each other. Scatterplots with R-squared values > 0.6 suggested aggressive UOF contributed to injuries of arrested residents across all races, Caucasian officers may have excessively injured arrested residents, and aggressive UOF correlated with arrests of African-Americans. Findings for parallel-coordinate-plots dove deeper in terms of spatial implications and ethical considerations (e.g., by visually demonstrating presence of a cluster of observed residents’ arrests as coinciding with African-American census geodemographics). This “small-sample” can surprise the reader. My conclusion proposed two aims: 1) solidify hypotheses (for further ESDA) that may induce ethical discomfort (a good thing) pertaining to the subject of structural racism, and 2) use findings to usher civic policymakers down more strident paths to sociocultural change.Indianapolis (IN) police districts and zones shapefiles that were made public by ESRI were used by way of my ESDA. Path to shapefiles’ source:http://data.indy.gov/datasets/indianapolis-police-zonesN.B.: Safari web-browser not recommended. Shapefile metadata are here: https://www.arcgis.com/home/item.html?id=b59421675f2a40fda9b00beeb875996fUsing GeoDa I did a spatial join that permitted my ESDA to analyze variables with scatterplots, PCPs, and datamaps. My final GeoDa file – titled NapWorksProj.gda – is herewith.Also herewith are my GeoDa's shapefiles – created natively – titled as follows:· NapWorks.cpg· NapWorks.dbf· NapWorks.prj· NapWorks.shp· NapWorks.shx

This dataset represents the base (ground-level) outline, or footprint, of buildings and other man-made structures in Fulton County, Georgia. The original data were produced by digitizing structures from 1988 aerial ortho-photography. Updates to the data are made from various aerial ortho-photography. In 2010, the data table structures was modified to include a number of attributes derived from tax assessment data through a spatial join of structures with tax parcels. The attributes include feature type (residential or commercial), structure form (conventional, ranch, colonial, etc.), number of stories, and the year built. In 2012, updates to features began using building sketch data collected by the Fulton County Tax Assessors. The building sketch data consist of turtle graphics type descriptors defining (in ungeoreferenced space) the ground-level outline of each structure in the County. These descriptors were converted to an ESRI SDE feature class using Python, georeferencing each structure by placing it in the center of its associated tax parcel. Each structure shape was is then manually translated and rotated into position using aerial imagery as a reference. As of May 2014, this update process was still in progress.This dataset is used in large-scale mapping to show the location of individual buildings and other man-made structures and in smaller-scale mapping to show general patterns of development. May also be used to estimate human population for very small areas. Other applications include the computation of impervious surfaces in stormwater studies and the development of 3-D urban models.

This shapefile provides a worldwide geographic division by merging the World Continents division proposed by Esri Data and Maps (2024) to the Global Oceans and Seas version 1 division proposed by the Flanders Marine Institute (2021). Though divisions of continents and oceans/seas are available, the combination of both in a single shapefile is scarce.

The Continents and Oceans/Seas shapefile was carefully processed to remove overlaps between the inputs, and to fill gaps (i.e., areas with no information) by spatially joining these gaps to neighbour polygons. In total, the original world continents input divides land areas into 8 categories (Africa, Antarctica, Asia, Australia, Europe, North America, Oceania, and South America), while the original oceans/seas input divides the oceans/seas into 10 categories (Arctic Ocean, Baltic Sea, Indian Ocean, Mediterranean Region, North Atlantic Ocean, North Pacific Ocean, South Atlantic Ocean, South China and Easter Archipelagic Seas, South Pacific Ocean, and Southern Ocean). Therefore, the resulting world geographic division has 18 possible categories.

References

Esri Data and Maps (2024). World Continents. Available online at https://hub.arcgis.com/datasets/esri::world-continents/about. Accessed on 05 March 2024.

Flanders Marine Institute (2021). Global Oceans and Seas, version 1. Available online at https://www.marineregions.org/. https://doi.org/10.14284/542. Accessed on 04 March 2024.

This layer contains American Community Survey (ACS) 2016-2020 5-year estimates in order to determine if a Census tract is considered an opportunity zone/low income community. According to Tax Code Section 45D(e), low income Census Tracts are based on the following criteria:The poverty rate is at least 20 percent, ORThe median family income does not exceed 80 percent of statewide median family income or, if in a metropolitan area, the greater of 80 percent statewide median family income or 80 percent of metropolitan area median family incomeThe layer is visualized to show if a tract meets these criteria, and the pop-up provides poverty figures as well as tract, metropolitan area, and state level figures for median family income. When a tract meets the above criteria, it may also qualify for grants or findings such Opportunity Zones. These zones are designed to encourage economic development and job creation in communities throughout the country by providing tax benefits to investors who invest eligible capital into these communities. Another way this layer can be used is to gain funding through the Inflation Reduction Act of 2022. The data was downloaded on October 5, 2022 from the US Census Bureau via data.census.gov:Table B17020: Poverty Status in the Past 12 Months - TractsTable B19113: Median Family Income in the Past 12 Months (in 2020 inflation-adjusted dollars) - Tracts, Metropolitan area, StateVintage of the data: 2016-2020 American Community SurveyBoundaries used for analysis: TIGER 2020 Tract, Metro, and State Boundaries with large hydrography removed from tractsData was processed within ArcGIS Pro 3.0.2 using ModelBuilder to spatially join the metropolitan and state geographies to tracts.To see the same qualification on 2010-based Census tracts, there is also an older 2012-2016 version of the layer.

For more information about LA County efforts to provide housing and tenant protections, see the DCBA website.This layer is distinct from similar upload in that this layer has supervisor district (sup_dist) and service planning area (spa) as attributes, based on "center in" spatial join.For more information about this dataset, please contact egis@isd.lacounty.gov

This repository contains a GeoPackage of edge-bundled line geometries between the centroids of all https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics" target="_blank" rel="noopener">NUTS 2 regions in continental Europe. The centroids of the NUTS 2 regions are derived from the 2021 version of the regions. The spatial layer contains just the edge-bundled lines, and no values for the flows. The coordinate reference system used is the https://epsg.io/3035" target="_blank" rel="noopener">ETRS89-extended / LAEA Europe (EPSG:3035) commonly used by The European Union.

This data is made to support the visualization of complex origin-destination matrix mobility data on the NUTS 2 level in Europe. Straight line geometries between origin and destination points can lose their legibility when the number of flows gets high.

Usage

To use the spatial layer, combine the provided GeoPackage with your origin-destination matrix data, such as migration, student exchange, or some other flow data. The edge-bundled flows has a directionality-preserving column for joining the flows (OD_ID). This can be done in QGIS/ArcGIS with a table join or in R/Python with a data frame merge.

Data structure

Production code

The spatial layer was produced by the https://doi.org/10.5281/zenodo.14532547">Edge-bundling tool for regional mobility flow data, which is a fork of a similar tool by Ondrej Peterka (2024), which is based on the work of Wallinger et al., (2022).

References