A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS

Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved

Spatio-temporal prediction of urban expansion using bivariate statistical models: assessment of the efficacy of evidential belief functions and frequency ratio models

The urban development process is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. Understanding urban expansion processes require models capable of simulating, monitoring, and predicting both urban growth and urban sprawl. In this research, probability-based Evidential Belief Functions (EBF) and Frequency Ratio (FR) models were employed to simulate and to predict the urban expansion probability map of the metropolitan area in Tripoli, Libya. These methods have not been used before in the urban development simulations of cities. By using the geographic information system (GIS), three satellite imageries obtained from 1996, 2002, and 2010 were employed to extract seven urban-deriving factors for the study area. The urban factors are slope, distance to active economic center, distance to central business district (CBD), distance to roads, distance to built-up areas, distance to educational area, and distance to coastal areas. For model calibration, both the EBF and FR models were applied to simulate urban expansion from 1996 to 2002. Data from 2002 to 2010 were used for models validation. Consequently, future suitability maps of urban growth were produced. The validation results indicated 83 % prediction accuracy for the EBF model and 84 % for the FR model. The outcomes established that the models could be employed in the urban expansion modeling of metropolises. The applied models, however, have dynamic and temporal limitations that should be considered in urban growth analysis

Spatio-temporal Analysis of Urban and Population Growths in Tripoli using Remotely Sensed Data and GIS.

The remote sensing data and GIS have been used widely to analyse and study the patterns of urban expansions. The capital of Libya, Tripoli was selected to perform this study and to examine its urban growth. Four satellite imageries and population censuses of the study area for the time period 1984 to 2010 were used in this work. The objectives of this paper are identifying and analysing the urban sprawl of Tripoli as a pattern and as process. Also to understand and assess the interchangeable relationship of urban growth and population growth of study area. Urban area extents in different time periods were extracted by supervised classification method of the satellite imageries. Then, the population data and urban extents data were coupled to perform the analysis. Additionally, Shannon's entropy technique was used for further assessment of urban growth. The study findings demonstrate that Tripoli had sprawled urban growth during the period 1984 to 2010. Moreover, during the above mentioned period, the urban expansion dispersion rate has shown in an ascending mode. Consequently, this uncontrolled dispersed urban development had resulted in high consumption land rate per capita despite of decrement in population growth rate

Urban expansion assessment by using remotely sensed data and the relative Shannon entropy model in GIS: a case study of Tripoli, Libya

Urban growth is a spatial dynamic phenomenon that indicates population growth, economic expansion, city importance level, and so on . The use of current and historical data in urbanization analysis is necessary in urban spatial studies and future urban planning. This research aims to study, examine, and assess the urban expansion of Tripoli spatially and temporally by using remotely sensed data, geographic information systems (GIS), and the statistical relative Shannon entropy model. Remotely sensed data (four satellite images from 1984, 1996, 2002, and 2010) and GIS were used to determine the extent of urban area and urban growth in Tripoli in five different directions. Shannon's entropy model was implemented to analyze and assess urban expansion trends as a process and pattern in the study area. Results show that the Tripoli metropolitan area has a high level of sprawl along its urban expansion history. The hypothesis employed for Shannon's entropy zone division produces good insights on overall urban growth, urban growth direction, and specific urban growth over time. The obtained results provide good guidance for modeling urban sprawl processes, understanding urbanization causative factors, and predicting future urban patterns. Furthermore, the findings of current paper can be used by decision makers and urban planners to identify past and present urban expansions tendencies to prepare for future urban demands

Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen.

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands

Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model

The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns

Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS

The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands

Quantitative analysis of urban sprawl in Tripoli using Pearson's chi-square statistics and urban expansion intensity index

Urban expansion is a spatial phenomenon that reflects the increased level of importance of metropolises. The remotely sensed data and GIS have been widely used to study and analyze the process of urban expansions and their patterns. The capital of Libya (Tripoli) was selected to perform this study and to examine its urban growth patterns. Four satellite imageries of the study area in different dates (1984, 1996, 2002 and 2010) were used to conduct this research. The main goal of this work is identification and analyzes the urban sprawl of Tripoli metropolitan area. Urban expansion intensity index (UEII) and degree of freedom test were used to analyze and assess urban expansions in the area of study. The results show that Tripoli has sprawled urban expansion patterns; high urban expansion intensity index; and its urban development had high degree of freedom according to its urban expansion history during the time period (1984-2010). However, the novel proposed hypothesis used for zones division resulted in very good insight understanding of urban expansion direction and the effect of the distance from central business of district (CBD)