Urban Land Uses

1933 ◽  
Vol 9 (1) ◽  
pp. 102
Author(s):  
Helen C. Monchow ◽  
Harland Bartholomew
Keyword(s):  
2019 ◽  
Vol 23 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Claudia Canedoli ◽  
Chiara Ferrè ◽  
Davide Abu El Khair ◽  
Emilio Padoa-Schioppa ◽  
Roberto Comolli

2019 ◽  
Vol 45 (2) ◽  
pp. 709
Author(s):  
J.D. Maldonado-Marín ◽  
L.C. Alatorre-Cejudo ◽  
E. Sánchez-Flores

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.


2017 ◽  
Vol 138 ◽  
pp. 9-15 ◽  
Author(s):  
A. Moges ◽  
A. Beyene ◽  
A. Ambelu ◽  
S.T. Mereta ◽  
L. Triest ◽  
...  

2019 ◽  
Vol 11 (7) ◽  
pp. 885 ◽  
Author(s):  
Ustaoglu ◽  
Aydınoglu

. Population growth, economic development and rural-urban migration have caused rapid expansion of urban areas and metropolitan regions in Turkey. The structure of urban administration and planning has faced different socio-economic and political challenges, which have hindered the structured and planned development of cities and regions, resulting in an irregular and uneven development of these regions. We conducted detailed comparative analysis on spatio-temporal changes of the identified seven land-use/cover classes across different regions in Turkey with the use of Corine Land Cover (CLC) data of circa 1990, 2000, 2006 and 2012, integrated with Geographic Information System (GIS) techniques. Here we compared spatio-temporal changes of urban and non-urban land uses, which differ across regions and across different hierarchical levels of urban areas. Our findings have shown that peri-urban areas are growing more than rural areas, and even growing more than urban areas in some regions. A deeper look at regions located in different geographical zones pointed to substantial development disparities across western and eastern regions of Turkey. We also employed multiple regression models to explain any possible drivers of land-use change, regarding both urban and non-urban land uses. The results reveal that the three influencing factors-socio-economic characteristics, regional characteristics and location, and development constraints, facilitate land-use change. However, their impacts differ in different geographical locations, as well as with different hierarchical levels.


2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali

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.


Author(s):  
Paul N. Balchin ◽  
Jeffrey L. Kieve ◽  
Gregory H. Bull
Keyword(s):  

1933 ◽  
Vol 46 (7) ◽  
pp. 1223
Author(s):  
Harland Bartholomew
Keyword(s):  

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