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).

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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.

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.

This record contains the underlying research data for the publication "Understanding the effects of taxi ride-sharing: A case study of Singapore" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/3968This paper studies the effects of ride-sharing among those calling on taxis in Singapore for similar origin and destination pairs at nearly the same time of day. It proposes a simple yet practical framework for taxi ride-sharing and scheduling, to reduce waiting times and travel times during peak demand periods. The solution method helps taxi users save money while helping taxi drivers serve multiple requests per day, thus increasing their earnings. A comprehensive simulation study is conducted, based on real taxi booking data for the city of Singapore, to evaluate the effect of various factors of the ride-sharing practice, e.g., waiting time, extra travel time, and taxi fare reduction. The results demonstrate that ride-sharing could serve 20%–25% more taxi booking requests and reduce traveler waiting time during peak hours. It also indicates that there is a reduction in travel distance of approximately 2–3 km per taxi trip on average, which is approximately 20%–30% of the average ride distance.