scholarly journals Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region

2019 ◽  
Vol 4 (1) ◽  
pp. 4 ◽  
Author(s):  
Muhammad Salem ◽  
Naoki Tsurusaki ◽  
Prasanna Divigalpitiya
Keyword(s):  
Logistic Regression ◽  
Population Density ◽  
Urban Areas ◽  
Urban Expansion ◽  
Driving Factors ◽  
Relative Operating Characteristic ◽  
Probability Map ◽  
Greater Cairo ◽  
Cell Window ◽  
Greater Cairo Region

The peri-urban area (PUA) of the Greater Cairo Region (GCR) in Egypt has witnessed a rapid urban expansion during the last few years. This urban expansion has led to the loss of wide, areas of agriculture lands and the annexation of many peripheral villages into the boundary of the GCR. This study analyzed the driving factors causing the urban expansion in the GCR during the period 2007–2017 using the logistic regression model (LRM). Eight independent variables were applied in this model: distance to the nearest urban center, distance to the nearest center of regional services, distance to water streams, distance to the main agglomeration, distance to industrial areas, distance to nearest road, number of urban cells within a 3 × 3 cell window and population density. The analysis was conducted using LOGISTICREG module in Terrset software. This research showed that the population density and distance to the nearest road have the highest regression coefficients, 0.540 and 0.114, respectively, and were the most significant driving factors of urban expansion during the last 10 years (2007–2017). Moreover, based on the results of the LRM, a probability map of urban expansion in the PUA was created, which shows that most urban expansion would be around the existing urban areas and near roads. The relative operating characteristic (ROC) value of 0.93 indicates that the probability map of urban expansion is valid.

scholarly journals Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

2021 ◽  
Vol 13 (19) ◽  
pp. 10805
Author(s):  
Muhammad Salem ◽  
Arghadeep Bose ◽  
Bashar Bashir ◽  
Debanjan Basak ◽  
Subham Roy ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Urban Areas ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Driving Factors ◽  
Sustainable Urban Development ◽  
Landsat Images ◽  
Relative Operating Characteristic ◽  
Simulation Based

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

scholarly journals Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017

2020 ◽  
Vol 12 (16) ◽  
pp. 2615
Author(s):  
Jie Zhang ◽  
Le Yu ◽  
Xuecao Li ◽  
Chenchen Zhang ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Hong Kong ◽  
Urban Areas ◽  
Urban Expansion ◽  
Driving Factors ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Spatial And Temporal Scales ◽  
Urban Dynamics ◽  
Bay Area

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

Modeling and prediction of expected informal growth in the Greater Cairo Region, Egypt

2021 ◽  
pp. 239980832110022
Author(s):  
Ibrahim Mohamed Badwi ◽  
Mohamed M El_Barmelgy ◽  
Ahmed Salah El_Din Ouf
Keyword(s):  
Growth Factors ◽  
Urban Areas ◽  
Informal Settlement ◽  
Informal Housing ◽  
Planning Models ◽  
Greater Cairo ◽  
New Methodologies ◽  
Developed Nations ◽  
Use Factors ◽  
Greater Cairo Region

Informal settlement growth is a vital challenge for developing countries, which requires monitoring and assessment by urban planners and city managers. Rural migration to urban areas leads to the unplanned expansion that grows within and beyond the city’s official boundaries. Although informal housing (IH) is growing fast, very little attention is oriented toward exploring tools and procedures for predicting its future expansion. Many studies have shown that informal housing is widespread and represents one of the most dominant features of urbanization in Egypt. Modern simulation and modeling technologies provide new methodologies to explore the complexity of urban growth. As a result, many planning models were developed and successfully used to simulate the spread of planned settlements in developed nations. However, the implementation of these models rarely achieves realistic simulation in the case of unplanned growth due to the developer’s field of study and the available resources. The main objective is to simulate the expected informal housing by modeling its causative land use factors in the Greater Cairo Region. This paper develops a predictive model that anticipates the spatial distribution of unplanned growth and where informal housing is likely to occur over a period based on known growth factors. The proposed Informal Housing Growth Model derives its principles from cellular automata and geographic information system technologies. This model uses a multi-criteria concept, including parameters and conditions related to informal growth, and can be adapted to other growth factors.

scholarly journals Preferensi Konsumen Terhadap Transportasi Publik (Studi Kasus Bus Rapid Transit (BRT) Kota Semarang)

2018 ◽  
Vol 33 (1) ◽  
Author(s):  
Ichwinsyah Azali ◽  
Edy Yusuf Agung Gunanto ◽  
Nugroho SBM
Keyword(s):  
Logistic Regression ◽  
Population Density ◽  
Public Transportation ◽  
Urban Areas ◽  
Binary Logistic Regression ◽  
Capital City ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Purposive Sampling ◽  
Transportation Modes

<p class="Headings1">Abstrak</p><p>Kota Semarang sebagai ibukota Provinsi Jawa Tengah memiliki tingkat kepadatan penduduk sebesar 4.269 jiwa/km2 pada tahun 2015. Dengan tingkat kepadatan yang cukup tinggi, mobilitas yang terjadi akan terus meningkat. Pemerintah mengatur kebijakan Lalu Lintas dan Angkutan Jalan melalui Undang-Undang Nomor 22 Tahun 2009  pasal 158 ayat 1, pemerintah kota Semarang menyediakan kebutuhan angkutan massal di kawasan perkotaan berupa <em>Bus Rapid Transit</em> (BRT) Trans Semarang. Penelitian ini bertujuan untuk menganalisis preferensi konsumen dari segi harga, kenyamanan, keandalan, aksesibilitas, dan keamanan terhadap kemungkinan pemilihan moda BRT dan moda transportasi Non-BRT. Penelitian ini menggunakan 100 responden dengan purposive sampling. Model <em>Binary Logistic Regression</em> digunakan untuk mengetahui pengaruh variabel independen terhadap variabel dependen pemilihan moda BRT dan Non-BRT. Hasil penelitian menunjukkan bahwa pemilihan moda BRT dan Non-BRT di Kota Semarang didominasi oleh pengguna 82 responden untuk BRT dan 18 responden untuk Non-BRT. Pemilihan moda BRT dan Non-BRT dipengaruhi oleh harga, faktor kenyamanan, keandalan, aksesibilitas dan keamanan.</p><p>Kata Kunci: Pemilihan Moda, <em>Bus Rapid Transit</em> (BRT) Trans Semarang, <em>Binary Logistic Regression</em>.</p><p> </p><p align="center"><strong><em>Abstract</em></strong></p><p><em>Semarang as the capital city of Central Java Province has a population density of 4.269 per square kilometer in 2015. Due to high population density, communities’ mobility will also be increasing. Government arranged the policy related to traffic and public transportation in the Law Number 22 Year 2009 article 158 paragraph 1, the government of Semarang City has guaranteed the availability of road-based transportation in Urban Areas by providing public transportation namely Bus Rapid Transit (BRT) Trans Semarang. This research aims to analyze communities’ preference in terms of price, convenience, reliability, accessibility, and safety toward the possibility of BRT and Non BRT transportation modes selection. This research uses 100 respondents by Purposive Sampling. Binary Logistic Regression model is applied to determine the effect of independent variable towards dependent variable of BRT and Non BRT transportation modes selection. The result of this research indicated that BRT and Non BRT transportation modes selection in Semarang City are dominated by 82 respondents of BRT users and 18 respondents of Non BRT users. BRT and Non BRT transportation modes selection are affected by the factor of price, convenience, reliability, accessibility, and safety.</em></p><p><strong><em>Keywords</em></strong><em>: Modes selection, Bus Rapid Transit (BRT) Trans Semarang, Principal Component Analysis, Binary Logistic Regression.</em><strong></strong></p><em class="Headings1"></em><strong></strong><em></em><strong></strong>

scholarly journals An Effective Framework for Monitoring and Measuring the Progress towards Sustainable Development in the Peri-Urban Areas of the Greater Cairo Region, Egypt

World ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Salem ◽  
Naoki Tsurusaki ◽  
Prasanna Divigalpitiya ◽  
Emad Kenawy
Keyword(s):  
Sustainable Development ◽  
Developing Countries ◽  
International Organizations ◽  
Urban Areas ◽  
Sustainability Indicators ◽  
Decision Makers ◽  
Urban Core ◽  
Greater Cairo ◽  
Wide Gap ◽  
Greater Cairo Region

Sustainable development (SD) has become a crucial challenge globally, particularly in developing countries and cities. SD of peri-urban areas (PUA) has been tackled by a limited number of studies, unlike that of urban areas or cities. The PUAs of Greater Cairo (GC) are no exception; no study had addressed the state of the PUAs in terms of SD. Thus, this study sought to measure and evaluate the progress towards the SD in the PUAs of Greater Cairo, Egypt. Thirteen indicators were extracted from selected documents of the competent international organizations to measure and evaluate the performance of SD in the study area. The study resulted in a variety of charts and maps to explain the progress of SD in each municipality of the PUAs and then classify these municipalities based on their performance in sustainability indicators. The results revealed a wide gap between PUAs’ municipalities and the urban core of Greater Cairo. These results can help urban planners and decision-makers to better recognize the underdeveloped areas on the Greater Cairo peripheries, and hence, to develop the appropriate strategies and policies to improve SD in such areas.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

scholarly journals A multiscale assessment of Red-tailed Hawk reproductive success relative to urban density and habitat type

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Justin H White ◽  
Jessi L Brown ◽  
Zachary E Ormsby
Keyword(s):  
Urban Areas ◽  
Urban Expansion ◽  
Habitat Type ◽  
Nesting Success ◽  
Arid Environments ◽  
Hatch Date ◽  
Urban Density ◽  
Landscape Characteristics ◽  
Suburban Areas ◽  
Breeding Seasons

Abstract Despite the unique threats to wildlife in urban areas, many raptors have established successfully reproducing urban populations. To identify variations in raptor breeding ecology within an urban area, we compared metrics of Red-tailed Hawk reproductive attempts to landscape characteristics in Reno and Sparks, NV, USA during the 2015 and 2016 breeding seasons. We used the Apparent Nesting Success and logistic exposure methods to measure nesting success of the Red-tailed Hawks. We used generalized linear models to relate nesting success and fledge rate to habitat type, productivity to hatch date (Julian day) and hatch date to urban density. Nesting success was 86% and 83% for the respective years. Nesting success increased in grassland-agricultural and shrub habitats and decreased in riparian habitat within the urban landscape. Productivity was 2.23 and 2.03 per nest for the breeding seasons. Fledge rates were 72% and 77%, respectively, and decreased in riparian areas. Nestlings hatched earlier with increased urban density and earliest in suburban areas, following a negative quadratic curve. Nesting success and productivity for this population were high relative to others in North America. Productivity increased in habitats where ground prey was more accessible. We suggest that suburban areas, if not frequently disturbed, provide sufficient resources to sustain Red-tailed Hawks over extended periods. As urban expansion continues in arid environments globally, we stress that researchers monitor reproductive output across the urban predator guild to elucidate patterns in population dynamics and adaptation.

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