Modeling Pedestrian Movement in Shopping Street Segments

This study explores the relative connections among pedestrian movement patterns, land use and street configuration by analyzing the pedestrian volume, existing land use pattern as well as the street configuration in different streets of Mymensingh. Mymensingh is a historic town of Bangladesh which was established by the British Colonists in more than 200 years ago along the river Brahmaputra. The street patterns of Mymensingh is very unique as it was developed by the fusion of the streets made by British Colonists and the narrow streets made by the local inhabitants. By using the method of Space Syntax the configurational values of street segments are compared with the corresponding pedestrian counts. The study result shows that the correlation is very poor within this two factors as commercial land uses affects the pedestrians more.

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Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Journal of Quantitative Criminology ◽

10.1007/s10940-021-09506-9 ◽

2021 ◽

Author(s):

John R. Hipp ◽

Sugie Lee ◽

Donghwan Ki ◽

Jae Hong Kim

Keyword(s):

Machine Learning ◽

Built Environment ◽

Google Street View ◽

Street View ◽

Street Segments

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Street Food Stand Availability, Density, and Distribution Across Income Levels in Mexico City

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18083953 ◽

2021 ◽

Vol 18 (8) ◽

pp. 3953

Author(s):

Jose B. Rosales Chavez ◽

Meg Bruening ◽

Punam Ohri-Vachaspati ◽

Rebecca E. Lee ◽

Megan Jehn

Keyword(s):

Mexico City ◽

Food Environment ◽

Local School ◽

Middle Income ◽

Street Food ◽

Income Levels ◽

Rigorous Approach ◽

Ground Truthing ◽

Document Relationships ◽

Street Segments

Street food stands (SFS) are an understudied element of the food environment. Previous SFS studies have not used a rigorous approach to document the availability, density, and distribution of SFS across neighborhood income levels and points of access in Mexico City. A random sample (n = 761) of street segments representing 20 low-, middle-, and high-income neighborhoods were assessed using geographic information system (GIS) and ground-truthing methods. All three income levels contained SFS. However, SFS availability and density were higher in middle-income neighborhoods. The distribution of SFS showed that SFS were most often found near homes, transportation centers, and worksites. SFS availability near schools may have been limited by local school policies. Additional studies are needed to further document relationships between SFS availability, density, and distribution, and current structures and processes.

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Hate Crime and Place: The Spatial and Temporal Concentration of Bias-Motivated Crime in Washington, D.C.

Journal of Interpersonal Violence ◽

10.1177/0886260520987817 ◽

2021 ◽

pp. 088626052098781

Author(s):

Marin R. Wenger ◽

Brendan Lantz

Keyword(s):

Hate Crime ◽

Hate Crimes ◽

Temporal Perspective ◽

Spatial Stability ◽

Units Of Analysis ◽

Crime Types ◽

High Degree ◽

Block Groups ◽

Street Segments

Prior research suggests that many crime types are spatially concentrated and stable over time. Hate crime, however, is a unique crime type that is etiologically distinct from others. As such, examination of hate crime from a spatial and temporal perspective offers an opportunity to understand hate crime and the spatial concentration of crime more generally. The current study examines the spatial stability of hate crimes reported to the police in Washington, D.C., from 2012 through 2018 using street segments, intersections, and block groups as units of analysis. Findings reveal that hate crime is spatially concentrated, with less than 4% of street segments and intersections experiencing hate crime over the study period. Results reveal a high degree of spatial stability, both year-to-year and over the long term even when restricting the analysis to units that experienced at least one hate crime.

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