scholarly journals Urban land use effect analysis on the level of noise pollution using satellite and GIS technologies: A case study in Tehran city

2020 ◽  
Keyword(s):  
Land Use ◽  
Remote Sensing Data ◽  
Noise Pollution ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Uses ◽  
Effect Analysis ◽  
Gis Technologies ◽  
The Relationship

<p>We utilized satellite and GIS technologies to address the relationship between urban land uses and Noise Pollution (NP) in one of the most crowded regions of Tehran city. Leq was determined in 170 stations of the studied area and an acoustic map was created. Moreover, using satellite remote sensing data and a land use map, the density map for nine types of the most important urban land use was provided and the relationship between different land use densities and NP was investigated. We found that the investigated region was highly polluted and the NP level was higher in the morning (76.29±5.61 dB[A]) and afternoon (76.46±4.88 dB[A]) in comparison to the noon period. Furthermore, the prepared acoustic map revealed that in the east and southwest of the studied area, the NP was highest and lowest, respectively. Also, cultural (73.48±4.7 dB[A]) and parking (79.02±4.3 dB[A]) areas had the lowest and highest levels of Leq. Also, the high density of road, commercial, industrial, mixed commercial and residential and parking land uses had a direct significant relationship with Leq and this relation was inverse for green space. It was concluded that land use variations significantly affect the NP levels and it can be utilized to predict and manage the NP in different cities.</p>

scholarly journals Land-Use Change and Health Risks in the Process of Urbanization: A Spatiotemporal Interpretation of a Typical Case in Changzhou, China

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 820
Author(s):  
Dongyang Yang ◽  
Chao Ye ◽  
Jianhua Xu
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Environmental Pollution ◽  
Environmental Health ◽  
Health Risks ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Land Expansion ◽  
Process Of Urbanization ◽  
The Relationship

China has undergone rapid urban expansion in recent decades. At the same time, environmental pollution and its risk to public health have increased. However, the relationship between urban land-use changes and health is ambiguous and insufficiently understood. Based on a typical city-scale case—namely, Changzhou, China—this research aimed to interpret the evolution of health risks alongside land-use change during the process of urbanization. We gathered data from multiple sources, including population mortality data, socioeconomic data, remote-sensing images, data for the points of interest of enterprises, and relevant information on environmental health events and cancers. The results showed that Changzhou’s urbanization was typical insofar as it was characterized by massive growth in industry, a rapid increase in the urban population, and urban land expansion. Health risks related to environmental pollution increased considerably with urban land expansion over time, and they increased with proximity to the pollution. The results from a generalized linear model confirmed that Changzhou’s urbanization triggered increasing health risks. Our study interpreted the relationship between urban land expansion and health risks from a spatiotemporal perspective. It can be used as a reference for urban planning and policymaking with regard to urban environmental health.

scholarly journals Identificación de conflictos de uso de suelo para la planificación del crecimiento urbano: ciudad Cuauhtémoc, Chihuahua (México)

2019 ◽  
Vol 45 (2) ◽  
pp. 709
Author(s):  
J.D. Maldonado-Marín ◽  
L.C. Alatorre-Cejudo ◽  
E. Sánchez-Flores
Keyword(s):  
Land Use ◽  
Development Planning ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Conflict ◽  
The Status ◽  
Resolution Model ◽  
Urban Planning And Development ◽  
Land Use Policies

This research incorporates new forms of analysis for urban planning and development in Ciudad Cuauhtémoc, Chihuahua (Mexico), providing elements of reference by identifying areas with potentiality and limitations for urban land use, as well as for agricultural and conservation activities. The general objective was to identify the main conflicts between land uses and coverages to determine the areas of greatest territorial suitability for the city's growth. For this purpose, the Land Use Conflict Identification Strategy (LUCIS) model was used to understand the spatial significance of the status of land use policies, including likely urban patterns associated with agricultural and conservation trends. In the case study, a total of 149,139 inhabitants are estimated for the year 2030, which represents the need for an additional 392.42 hectares to accommodate the population growth. For that of the 16,272.21 hectares that has the population limit, 38 % were allocated to the category of agriculture, 11.95% to conservation soils and 49.67% to urban land (including the existing urban area). There is a significant portion of the area that is in conflict between the different land uses. It concludes, that the integration of a conflict resolution model for land use and land cover represents a practical solution that contributes to the improvement of processes of urban development planning.

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

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