Computers, Environment and Urban Systems

This dataset includes three excel sheets containing the original neighborhood aggregation index (NAI) calculated for sampled points for urbanized land cover type, at different neighborhood sizes (3×3, 5×5, 7×7, 9×9, and 11×11) and time windows (T1: 2016, T2: 2015-2016, and T3: 2014-2016).

See Readme

A case study is conducted in Yau Tsing Mong District, Hong Kong SAR, which involves 1155 street-links and 41,838 street-level images. Processed by PSPnet, the table shows the proportion of 150 object categories for each of 1155 street-links.

The implemented model is described in the companion paper

This repository contains spatial data on greenspaces, the zones they are accessible to within walking distance, the surrounding road network, surrouding educational facilities and residential environments, and estimated commuting routes between educational and residential environments. This data was collected through Python, using Jupyter Notebooks, and applies to four cities in The Netherlands: Amsterdam, Rotterdam, The Hague, and Delft. For Delft, only greenspaces and the zones they are accessible to are incorporated, as they were collected as additional examples for a side-project to this research.

Notebooks 1-9 in this repository allow you to calculate the accessibility of urban greenspaces by children and adolescents. It calculates accessibility not only to home locations, but also to educational facilities (e.g., schools, colleges, and university buildings), and during the commute in between them. This code is developed for three cities in The Netherlands (i.e., Amsterdam, Rotterdam, and The Hague) and can be adapted to fit other geographical contexts.Additional examples: Notebooks to generate pedestrian walksheds around greenspaces; to compare effects of different greenspace accessibility measures; and to generate commuting heatmaps for cohort data.

This repository contains spatial data on greenspaces, the zones they are accessible to within walking distance, the surrounding road network, surrouding educational facilities and residential environments, and estimated commuting routes between educational and residential environments. This data was collected through Python, using Jupyter Notebooks, and applies to four cities in The Netherlands: Amsterdam, Rotterdam, The Hague, and Delft. For Delft, only greenspaces and the zones they are accessible to are incorporated, as they were collected as additional examples for a side-project to this research.

This repository contains spatial data on greenspaces, the zones they are accessible to within walking distance, the surrounding road network, surrouding educational facilities and residential environments, and estimated commuting routes between educational and residential environments. This data was collected through Python, using Jupyter Notebooks, and applies to four cities in The Netherlands: Amsterdam, Rotterdam, The Hague, and Delft. For Delft, only greenspaces and the zones they are accessible to are incorporated, as they were collected as additional examples for a side-project to this research.

Notebooks 1-9 in this repository allow you to calculate the accessibility of urban greenspaces by children and adolescents. It calculates accessibility not only to home locations, but also to educational facilities (e.g., schools, colleges, and university buildings), and during the commute in between them. This code is developed for three cities in The Netherlands (i.e., Amsterdam, Rotterdam, and The Hague) and can be adapted to fit other geographical contexts.Additional examples: Notebooks to generate pedestrian walksheds around greenspaces; to compare effects of different greenspace accessibility measures; and to generate commuting heatmaps for cohort data.

Notebooks 1-9 in this repository allow you to calculate the accessibility of urban greenspaces by children and adolescents. It calculates accessibility not only to home locations, but also to educational facilities (e.g., schools, colleges, and university buildings), and during the commute in between them. This code is developed for three cities in The Netherlands (i.e., Amsterdam, Rotterdam, and The Hague) and can be adapted to fit other geographical contexts.Additional examples: Notebooks to generate pedestrian walksheds around greenspaces; to compare effects of different greenspace accessibility measures; and to generate commuting heatmaps for cohort data.