Driving factors of urban sprawl in Giza governorate of the Greater Cairo Metropolitan Region using a logistic regression model

被引:29
|
作者
Osman, Taher [1 ,2 ]
Divigalpitiya, Prasanna [3 ]
Arima, Takafumi [4 ]
机构
[1] Kyushu Univ, Dept Architecture & Urban Design, Fukuoka, Japan
[2] Cairo Univ, Fac Urban & Reg Planning, Giza, Egypt
[3] Kyushu Univ, Fac Human Environm Studies, Fukuoka, Japan
[4] Saga Univ, Sch Engn, Dept Urban Engn, Saga, Japan
关键词
Urban sprawl; driving factors; logistic regression; GCMR;
D O I
10.1080/12265934.2016.1162728
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Since the 1950s, The Greater Cairo Metropolitan Region (GCMR) has witnessed an unprecedented rate of urban sprawl that has been mainly concentrated in arable lands against urban planning regulations, and has presented a critical challenge to the urban environment and serious corrosion for arable lands. Thus, the need to identify the driving factor of sprawl is crucial to understand the future of the GCMR urban environment and to overcome the serious challenges of rapid urbanization. We focused on the Giza governorate as a critical area in the GCMR and divided it into three sub-sectors to collect data and analyse. A primary list of driving factors was identified by literature review. Later this list was narrowed down to seven factors after interviews with local urban experts and consideration of the availability of data. Next, a logistic regression analysis was used to evaluate those factors with data derived from existing maps and remotely sensed data for the period of 2004-2013. An operating characteristic (ROC) evaluation of the logistic regression analysis gave high accuracy rates for the entire study area. The findings of the research revealed decreasing significance of the CBD and Nile River as drivers of sprawl. The most significant factors according to the analysis were neighbourhood factors, local urban centres, and accessibility factors of distances to urban uses and major roads. The research suggests more future urban expansion by the existing urban cores and along major roads, leading to more informal urban settlements. It also points to the possibility of persistent deterioration in the urban built environment and agricultural lands. Thus, these findings should be applied to actual urban planning policies, and development regulations should be strengthened to protect the urban environment from further deterioration.
引用
收藏
页码:206 / 225
页数:20
相关论文
共 50 条
  • [31] Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model
    Shivani Chauhan
    Mukta Sharma
    Manoj K. Arora
    Landslides, 2010, 7 : 411 - 423
  • [32] Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model
    Chauhan, Shivani
    Sharma, Mukta
    Arora, Manoj K.
    LANDSLIDES, 2010, 7 (04) : 411 - 423
  • [33] A habitat model for Parus major minor using a logistic regression model for the urban area of Osaka, Japan
    Hashimoto, H
    Natuhara, Y
    Morimoto, Y
    LANDSCAPE AND URBAN PLANNING, 2005, 70 (3-4) : 245 - 250
  • [34] Land Cover Mapping Analysis and Urban Growth Modelling Using Remote Sensing Techniques in Greater Cairo Region-Egypt
    Megahed, Yasmine
    Cabral, Pedro
    Silva, Joel
    Caetano, Mario
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2015, 4 (03) : 1750 - 1769
  • [35] Using a Logistic Regression Model to Analyze Alley Farming Adoption Factors in Sierra Leone
    Alhaji M. H. Conteh
    Juana P. Moiwo
    Xiangbin Yan
    Small-scale Forestry, 2016, 15 : 109 - 125
  • [36] Using a Logistic Regression Model to Analyze Alley Farming Adoption Factors in Sierra Leone
    Conteh, Alhaji M. H.
    Moiwo, Juana P.
    Yan, Xiangbin
    SMALL-SCALE FORESTRY, 2016, 15 (01) : 109 - 125
  • [37] URBAN GROWTH MODELING OF PHNOM PENH, CAMBODIA USING SATELLITE IMAGERIES AND A LOGISTIC REGRESSION MODEL
    Mom, Kompheak
    Ongsomwang, Suwit
    SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY, 2016, 23 (04): : 481 - 500
  • [38] Landslide susceptibility zonation of Tehri reservoir rim region using binary logistic regression model
    Kumar, Rohan
    Anbalagan, R.
    CURRENT SCIENCE, 2015, 108 (09): : 1662 - 1672
  • [39] Analysis of the Driving Forces of Urban Expansion Based on a Modified Logistic Regression Model: A Case Study of Wuhan City, Central China
    Luo, Ti
    Tan, Ronghui
    Kong, Xuesong
    Zhou, Jincheng
    SUSTAINABILITY, 2019, 11 (08):
  • [40] Discovering Potential Driving Factors of Smart Thermostat Hold Behaviors Using a Mixture of Logistic Regression Models and Bayesian Inference
    Deng, Yufeng
    Lee, Seungjae
    Touchie, Marianne F.
    ASHRAE TRANSACTIONS 2022, VOL 128, PT 2, 2022, 128 : 255 - 263