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 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

Data Use: An enriched layer of City Council Districts (as of 2023) with metrics for supporting urban agriculture such as need (e.g, the SVI and dietary health), community assets (e.g., places of faith, meal assistance programs, public housing), land opportunities (e.g., vacant parcels), and existing urban agriculture projects. These metrics were aggregated from other features within the City Council Districts such as tract centroids and point locations. The pop-up for this layer provide a broad overview for urban agriculture potential within a District. The attribute table can be used to build District-level charts and tables. The layer was first downloaded by the provider from its source and enriched with spatial joins in ArcGISPro before being uploaded back into the AGOL environment. The layer is designed as a contextual layer in the webmap, COD Social Health UA. Data source: City of Dallas GIS Services, Current Council Districts - Polygon, COD_DistrictBoundariesFullDataYear: 2023Provider: FHEED, LLCRelated: COD Social Health UA, ID: ed08af809e9f4a968713fcb0e8cf8750

Data Use: These are census tracts within the City of Dallas that have metrics to potentially support urban agricultures such as need (via the CDC's SVI), land opportunities (e.g., vacant parcel acres), community assets (e.g., counts of assisted meal sites and HUD buildings), and counts of existing urban agriculture sites. The counts for these urban agriculture factors were created by spatially joining their point locations to the tracts in ArcGISPro. The calculations were used to derive the current versions of the Urban Agriculture Priority Areas (UAPAs), which are outlined in the 2023 Comprehensive Urban Agriculture Plan (CUAP). This layer evolved from a previous version, FullTractMetrics_2_10_22, which is no longer used in any mapping products. The current version is used to produce other copy layers in its host map, COD Social Health UA, that visualize diabetes rates, Social Vulnerability Index scores, rates of urban gardens and several other metrics related to urban agriculture. Attributes in this layer are also connected to widgets in the webmap application, City of Dallas Urban Agriculture and Community Health Explorer. Therefore, any changes in this layer may impact the functionality of that application. The attribute table in this layer was used to produce the technical analysis in the CUAP. This layer will need to be updated to produce new UAPAs. Data source: Shapefile is from DallasEGIS_1 via the Dallas Open Data PortalYear: 2010 (Shape), 2019 (CDC Data), 2022 (spatial join data)Provider: FHEED, LLCRelated: COD Social Health UA, ID: ed08af809e9f4a968713fcb0e8cf8750; City of Dallas Urban Agriculture and Community Health Explorer, ID: 4c4d5625df354c54b9c4fe205050592f

Map Series of multiple analysis procedures performed in ArcGIS Pro and ArcGIS Online. Analysis performed are: Pairwise Clip, Hot Spot Analysis. Join-Spatial Layers, and Colocation.Maps in series:1. Active Faults2. Population of Seattle Metropolitan Area3. Vulnerability Map,4. Colocation Analysis of Unreinforced Buildings City of Seattle5. Seattle Fault Shake Intensity Map6. Population Seattle Fault Earthquake7. Seattle Fault Vulnerability Map8. Cascadia Subduction Zone Shake Intensity Map9. Population Cascadia Subduction Zone Earthquake10. High Risk Vulnerability Cascadia Fault Earthquake Event11. Cascadia Subduction Zone Tsunami Energy (1700 Earthquake Event)12 Tsunami Impact and Population13. At Risk Population Tsunami Impact14. Highest Risk Vulnerability

This layer shows urban bike corridors for York County, Pennsylvania. They are dedicated or prioritized pathways designed to enhance cyclist safety, accessibility, and connectivity within city transportation networks. Bike corridors may include protected bike lanes, shared roadways, multi-use trails, and greenways, often integrated with public transit and urban land uses. They are typically designed with features such as traffic calming, signage, pavement markings, and physical barriers to promote safe, continuous, and comfortable cycling for all age and ability levels. The analysis incorporates spatial data layers such as road networks, digital elevation models (DEM), land use/land cover (LULC), vehicular traffic volumes, cycling crash incidents, and demographic data within GIS platforms like ArcGIS Pro and QGIS. Advanced network analysis, including least-cost path modeling and service area analysis, identifies optimal routing options based on factors such as slope, traffic density, and road hierarchy. Kernel Density Estimation and hotspot analysis help locate high-risk zones for cyclists, informing the placement and design of safer corridors. Suitability analysis is conducted through a weighted overlay of criteria—proximity to key destinations (schools, parks, employment centers), existing cycling infrastructure, equity indicators, and environmental considerations. GIS tools such as buffering, intersecting, and spatial joins are used to model catchment areas and potential demand. A Multi-Criteria Decision Analysis (MCDA) framework within GIS enables the prioritization of corridor segments based on user-defined planning goals. The output includes thematic maps, spatial prioritization models, and a comprehensive geodatabase of recommended bike corridor alignments. This GIS-based approach offers city planners and policymakers a robust, data-driven toolset for designing bike-friendly cities that promote active transportation, reduce carbon emissions, and support healthier, more equitable communities.