This feature layer depicts the proposed study area of the Austin Light Rail Project. This can be used in maps and interactive viewers.Data Owner & Organization: Austin Transit Partnership - Planning, Community, & Federal Programs teamData Source Details: This dataset was created using the buffer geoprocessing tool to buffer the LRT alignment 0.5 miles.Data Refresh Schedule: This data was used for the Implementation Plan published in May 2023. It will not be refreshed. ATP Data Classification: Public; this data can be shared publicly.

The method to create the Wind Resource Area datasets is to:Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

Information contained includes project ID, flight start date, flight end date, index confirmation, camera type, project type, production status, and whether the flight has been geo-referenced or not.

This polygon feature class was created to model the flight coverage of geo-rectified aerial photography for the State Water Project for each flight group. The coordinate system utilized for this feature class is NAD1983_CaTM. The purpose for the creation of this dataset is to help classify the various flight groups according to the corresponding project ID and supply relevant spatial data information. To create the photo centers, archived aerial photography was first scanned and converted to .tiff file format that would be later geo-referenced to NAIP 2016. The photo centers at the ends of each fight line would be marked using the polygon drawing tool by creating an "X" from the vertex of each corner fiducial. The flight line would then be created using these photo center for end points with a line feature class. The photo centers were then created by constructing points using the editor tool. To develop the coverage extent, the photo scale was inputted into an excel flight plan distance calculator to formulate coverage distance. Next, the buffer tool under the geo-processing tab was utilized to input the coverage distance found previously to create the flight coverage area.

Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

After facilities were grouped, the outermost facilities in each region were connected to create the boundaries for each region. To include all facilities inside each polygon, a 10 km buffer was used. Plants that are farther from a concentration of other plants are not included in a region and are instead displayed as outlying facilities. For more information, please contact John Hingtgen (916) 510-9747.Wind Generation Regions (10/7/2021)A heuristic model that combines a statistical algorithm, manual digitization, and cartographic refinement created the wind generation regions. The produced regions depict spatially similar utility-scale wind which are useful in discussing said resources. The statistical algorithm used is Multivariate Clustering, an ESRI ArcGIS Pro tool in the Geoprocessing package. The clustering technique finds statistically similar features after the user defines the parameters to be compared between clusters, the preferred number of groups, and the clustering type. The constriction parameters are weighted-factors that further refine the groups. The user designates the number of groups (in this case I defined this as 6; 5 clusters and 1 group for outlying facilities), but the algorithm may also calculate the optimal number if desired. More specifically, for this project the K-means clustering is used which divides objects into clusters which are “similar” between them and are “dissimilar” to the objects belonging to another cluster. K-means is an unsupervised learning algorithm used for analyzing and grouping data which does not include pre-labeled class or even a class attribute at all. The clustering technique is not a perfect tool and needs to be refined by, for example, increasing the number of clusters, or by adding more comparison parameters. For this project, the k-means technique operated at an 85% accuracy. After completing this analysis, the next step required manually adjusting each cluster. By doing so, we hoped to increase the accuracy of clusters and make them more whole and coherent. At the end of this process, all facilities in the state had been given a bespoke number signifying their clusters.After clusters were created, they were used to form polygons. This was done with the help of RStudio, using multiple packages including ‘sf’, ‘maptools’, ‘dplyr’, and ‘foreign’. In short, the script connected the outer most facilities for each region to create a region. This yielded jagged regions, which subsequently underwent a smoothing process to improve the level of cartographic aesthetic. To do so, ArcGIS Pro’s “Buffer” tool was used. The buffer around region was specified as 10 kilometers. The resulting eight regions represent more than 99% of solar resources, with 2 facilities defined as outliers. Although these two facilities are located close to one another they do not form a region because to create a region statistically there needs to be at least four points (or in this case facilities) that would determine the size and the shape of the region.