Result of overlay analysis of all 28 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are nine, reflecting that in these cells features from nine different map layers overlap. The summary analysis was performed as a so-called GIS overlay analysis using custom-made Python scripts in ArcGIS Pro 3.0.2. In principle, the different map layers presented in Chapters 4-6 were simply stacked on top of each other, and for each 250x250 m cell in a grid system covering the entire AOI, the number of map layers with features present in the cell were counted. Thus, a resulting cell value of e.g., 3 indicates that at the centre of the cell three different map layers have features present. In rare cases, an individual layer may have several features present at the cell centre, e.g., two cultural heritage zone 3 areas, but the layer will still only add a value of one to the overlay. Thus, it is the number of different layer with features present that is summarised, not the number of individual features.

The Strategic Areas portrayed in this data set are based on the FY 2015-16 Strategic Investment Areas Overlay Analysis, which is part of the City of Austin’s Long-Range Capital Improvement Program Strategic Plan (LRCSP)*. Due to the limitations of the platform, the data is not displayed how it is meant to be viewed. Please see the dark red “About” tab for a thumbnail of how the map is ideally viewed and two attachments. One attachment is a description of the metadata and the other attachment provides instructions for how to view the data properly. The Strategic Areas show the degree to which City of Austin initiatives overlap each other geographically, representing opportunities for collaboration and coordination of capital investment related to implementing the Imagine Austin Comprehensive Plan. The data set is comprised of initiatives that have been formally adopted by City Council or are from department-approved planning documents/reports. Examples include the Imagine Austin Growth Concept map, City Council-approved infrastructure master plans (i.e. the City of Austin Sidewalk Master Plan), City Council-approved Neighborhood Plans, and City Council and/or departmental accepted reports (i.e. the Hispanic-Latino Quality of Life Initiative final report). These initiatives contain recommendations for areas to invest in or focus on certain capital improvements, the geographical boundaries of which are represented in the data set. The areas with the greatest number of overlapping initiatives are indicated by the darker shades, representing areas where capital investment can have a multiplier effect in implementing several city initiatives. The data is organized around Imagine Austin Priority Program topic areas: Compact and Connected, Nature and City, Creativity and Economy, Healthy and Affordable. The purpose of the LRCSP Strategic Investment Areas Overlay Analysis is to support the identification of: (a) Strategic investment in infrastructure that implements Imagine Austin, (b) Potential cost savings and other efficiencies and (c) Improved outcomes for our capital investments. NOTE: the areas are updated periodically to respond to changes in City plans and policies as reflected in City Council-approved initiatives. *The Long-Range CIP Strategic Plan is produced annually, and the Planning Commission uses the plan to make its annual recommendation to the City Manager about projects necessary or desirable to implement the comprehensive plan, per its role in the City Charter. Learn more about the plan or download it at Austintexas.gov/strategicplan.

Summary:

This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable.

The contained files were developed by the NY Cities Program of The Nature Conservancy and the NYC Environmental Justice Alliance through the Just Nature NYC Partnership. Additional context and interpretation of this work is available in a blog post.

References:

Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Climate 31:100583. https://doi.org/10.1016/j.uclim.2020.100583

Files in this Repository:

Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, EPSG 2263):

Points with unique identifiers (fid) and data on proportion tree canopy cover (prop_canopy), proportion grass/shrub cover (prop_grassshrub), and proportion total vegetation cover (prop_veg) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg

Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)

nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif

Computer Code:

Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.

nyc_point_buffer_vegetation_overlay.sql

Data Sources and Methods:

We used two openly available datasets from the City of New York for this analysis:

Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page

Six-inch resolution land cover data for New York City as of 2017, available at https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns

All data were used in the New York State Plane Coordinate System, Long Island Zone (EPSG 2263). Land cover data were used in a polygonized form for these analyses.

The general steps for developing the data available in this repository were as follows:

Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions

Delete any points that do not overlap the areas in the Borough Boundaries dataset.

Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).

Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.

Relate the results from the overlay analysis back to the associated points.

Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."

All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in nyc_point_buffer_vegetation_overlay.sql. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the gdal_rasterize function.

We present research conducted in the framework of the European project H2020 on cybercrime and minors with the aim of analyzing the prevalence of and relationship between cyberbullying and online hate speech among adolescents in two different areas of Europe (Spain −South- and Estonia −North-). We implemented a representative survey in the region of Madrid (Spain, n = 682) and Estonia (n = 415) with a stratified probability sampling method. We analysed frequencies together with a bivariate analysis and logistic regression. The results show a similar general prevalence in cyberbullying victimization, but online insults were more common in Estonia and account takeover and exclusion from a group were more common in Spain. However, online insults, racism, and LGTBIphobia had a higher difference in perpetration prevalence in Estonia. While common risk factors for victimization were being a female, being LGTBI, and spending more than three hours online, the leading risk factor for perpetrating was being male. Finally, there was strong overlap between being a cyberbullying and a cyberhate offender. We suggest some potential explanations for these differences: the extent of technological implementation in the region and the time spent online, information provided in the school and at home, and culturally predominant racism and LGTBphobia. The overlap between being a cyberbullying and a cyberhate offender, the gender- and sexual orientation-related risk factors, and the regional differences in prevalence show the importance of addressing the social and cultural aspects of online violence and the importance of social inequalities and power imbalance.

Overlay of medium, high and very high agricultural potential and yield gaps for Bangladesh. Indicators: Suitability of land area for rainfed crops: FGGD map 6.60, classes: medium to very high Yield Gaps: FAO/IIASA - GAEZ < 0.5 The here presented simple analysis should give insights in potential suitable areas for expecting high yields. Yield gap information for Bangladesh further showed were gaps for production occur. By resampling the statistical data into a raster dataset an overlap with suitability information based on sateliite imagery was possible. The overlay was processed in ArcGIS classifying those areas where yield gaps occur with suitability areas as defined by the dataset cited above. Yield gaps can be found in the southern part of Bangladesh. More intersting are the dark green areas where according to satellite information high potential areas are located but not as many yields are produced. Here, more detailed analysis might be useful.