scholarly journals Measuring Community Greening Merging Multi-Source Geo-Data

2019 ◽  
Vol 11 (4) ◽  
pp. 1104 ◽  
Author(s):  
Weiying Gu ◽  
Yiyong Chen ◽  
Muye Dai
Keyword(s):  
Spatial Distribution ◽  
Spatial Relationship ◽  
Image Data ◽  
Urban Land Use ◽  
Residential Areas ◽  
Residential Community ◽  
Shenzhen City ◽  
Spatial Distribution Characteristics ◽  
Residential Units ◽  
Green Land

Urban residential greening provides opportunities for social integration and physical exercise. These activities are beneficial to promoting citizens’ mental health, relieving stress, and reducing obesity and violent crimes. However, how to measure the distribution and spatial difference of green resources in urban residential areas have been controversial. This study takes the greening of urban residential units in Shenzhen City as its research object, measures the various greening index values of each residential unit, and analyses the spatial distribution characteristics of residential greening, regional differences, and influencing factors. A large sample of street view pictures, urban land use and high-resolution remote sensing image data are employed to establish an urban residential greening database containing 14,196 residential units. This study proposes three greening indicators, namely, green coverage index, green view index, and accessible public green land index, for measuring the green coverage of residential units, the visible greening of surrounding street space and the public green land around, respectively. Results show that (1) the greening level of residential units in Shenzhen City is generally high, with the three indicators averaging 32.7%, 30.5%, and 15.1%, respectively; (2) the types of residential greening differ per area; and (3) the level of residential greening is affected by development intensity, location, elevation and residential type. Such findings can serve as a reference for improving the greening level of residential units. This study argues that one indicator alone cannot measure the greenness of a residential community. It proposes an accessible public green land index as a measure for the spatial relationship between residential units and green lands. It suggests that future green space planning should pay more attention to the spatial distribution of green land, and introduce quantitative indicators to ensure sufficient green lands around the walking range of residential areas.

scholarly journals How green are residential areas? An analysis of community greening emerging multi-source geo-data

2018 ◽  
Author(s):  
Yiyong Chen
Keyword(s):  
Spatial Distribution ◽  
Spatial Relationship ◽  
Image Data ◽  
Urban Land Use ◽  
Residential Areas ◽  
Space Planning ◽  
Shenzhen City ◽  
Spatial Distribution Characteristics ◽  
Residential Units ◽  
Green Land

AbstractUrban residential greening provides opportunities for social integration and physical exercise. These activities are beneficial to promoting citizens’ mental health, relieving stress and reducing obesity and violent crimes. However, how to measure the distribution and spatial difference of green resources in urban residential areas have been controversial. This study takes the greening of urban residential units in Shenzhen City as its research object, measures the various greening index values of each residential unit, and analyses the spatial distribution characteristics of residential greening, regional differences and influencing factors. Large sample of street view pictures, urban land use and high-resolution remote sensing image data are employed to establish an urban residential greening database containing 14,196 residential units. This study proposes three greening indicators, namely, green coverage index, green view index, and accessible public green land index, for measuring the green coverage of residential units, the visible greening of surrounding street space and the public green land around, respectively. Results show that (1) the greening level of residential units in Shenzhen City is generally high, with the three indicators averaging 32.7%, 30.5% and 15.1%, respectively; (2) the types of residential greening differ per area; and (3) the level of residential greening is affected by development intensity, location, elevation and residential type. Such findings can serve as a reference for improving the greening level of residential units. This study proposes an accessible public green land index as a measure for the spatial relationship between residential units and surrounding green spaces. It suggest that future green space planning should pay more attention to the spatial distribution of public green land, and introduce quantitative indicators to ensure sufficient public green lands around the walking range of residential areas.

IMPACTS OF URBAN LAND USE ON CRIME PATTERNS THROUGH GIS APPLICATION

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Ahmad Nazri Muhamad Ludin ◽  
Norsiah Abd. Aziz ◽  
Nooraini Hj Yusoff ◽  
Wan Juliyana Wan Abd Razak
Keyword(s):  
Land Use ◽  
Drug Abuse ◽  
Land Use Planning ◽  
Urban Areas ◽  
Spatial Relationship ◽  
Police Department ◽  
Strong Relationship ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Patterns

Land use planning plays a crucial role in creating a balance between the needs of society, physical development and the ecosystem. However, most often poor planning and displacement of land uses particularly in urban areas contribute to social ills such as drug abuse and criminal activities. This research explains the spatial relationship of drug abuse and other criminal activities on urban land use planning and their implications on the society at large. Spatial statistics was used to show patterns, trends and spatial relationships of crimes and land use planning. Data on crime incidents were obtained from the Royal Malaysia Police Department whilst cases of drug abuse were collected from the National Anti-Drug Agency (AADK). Analysis of the data together with digital land use maps produced by Arnpang Jaya Municipal Council, showed the distribution of crime incidents and drug abuse in the area. Findings of the study also indicated that, there was a strong relationship between petty crimes, drng abuse and land use patterns. These criminal activities tend to concentrate in residential and commercial areas of the study area.

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

Sign in / Sign up

Export Citation Format

Share Document