Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city

The Egyptian Journal of Remote Sensing and Space Sciences 22 (2019) 59–66

Contents lists available at ScienceDirect

The Egyptian Journal of Remote Sensing and Space Sciences journal homepage: www.sciencedirect.com

Research Paper

Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city Hatem Mahmoud a,⇑, Prasanna Divigalpitiya b a b

Dept. of Architecture, Faculty of Engineering, Aswan University, Egypt Dept. of Architecture and Urban Design, Faculty of Human Environment Studies, Kyushu University, Japan

a r t i c l e

i n f o

Article history: Received 10 February 2017 Revised 19 February 2018 Accepted 20 March 2018 Available online 14 December 2018 Keywords: Land control policy Logistic regression New city Urban sprawl Egypt Asyut

a b s t r a c t The Egyptian government set out in the 1970s to establish new cities in the desert to absorb urban sprawl and to prevent further depletion of agricultural lands. Despite such efforts, this policy has not met with much success. In the present study, LANDSAT satellite imagery and binary logistic regression analysis were employed to investigate the nature of urban sprawl in one of the most important cities in Egypt, Asyut city, as well as the area connecting it to New Asyut city by quantifying the interaction between the driving forces of land use/cover change. Various socioeconomic factors associated with land control policy were examined. The results indicated that whereas directing urban development towards the new city created a semblance of balance initially, the rate of land development in the study area outstripped the rate of population growth, especially in the new city. While establishment of the new city boosted early urban development, further development in the regions outside the Nile valley was not sustained due to a lack of supportive governmental policies. Consequently, urban residents moved back inside the valley to re-settle on agricultural lands adjacent to the old urban cores. This study is the first to quantify the driving forces of land use in this region. It offers useful data to guide planned and purposeful expansion of urban land by government policy-makers in their effort to curb urban sprawl and prevent further encroachment on agricultural land in Egypt. Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

1. Introduction During the last twenty-five years, Egyptian cities and villages have witnessed unprecedented growth, resulting in poorly planned development of agricultural lands, especially in areas outside the formal urban (administrative) boundaries identified in 1985 (Madboly, 2005). The rapid urban sprawl has caused agricultural lands to be decreased by 36% or about 1.5 million acres (6300 million m2) in Egypt (Rageh, 2007). Taking cognizance of the need to contain the urban sprawl, the government launched a new town policy in the1970s that aimed to establish new towns in the desert and areas previously allocated for agricultural use. The government

Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. ⇑ Corresponding author at: Faculty of Engineering, Aswan University, Aswan 81542, Egypt. E-mail addresses: [email protected] (H. Mahmoud), prasanna@ arch.kyushu-u.ac.jp (P. Divigalpitiya).

was aware that the old inhabited areas along the Nile valley were no longer able to absorb the increasing population, and that Egyptians had to exploit their desert regions in order to achieve more sustainable development. Hence, the aims of developing new towns were to re-settle urban dwellers in areas with low population density, create an industrial base outside the valley, and attract new public and private investments (Aafify, 1999). By 2013, there were 20 new towns which were either already functioning or under construction, and more than 40 new cities and communities were being planned (Hegazy and Moustafa, 2013). However, the government’s plan did not work as intended as most of the new cities failed to attract the target population and development. Hence, the sprawl still prevailed in old cities and villages. For example, at one center, Markaz El Fath, in the Asyut governorate of upper Egypt, agricultural land was displaced by housing in a total of 391 acres (about 0.2% of the region of El Fath) between 2004 and 2008 (Fig. 1) (ETH Studio Basel, 2009). The study area was the region between Asyut center (a center is an administrative division that included a major city, and small cities and villages)

https://doi.org/10.1016/j.ejrs.2018.03.006 1110-9823/Ó 2018 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

60

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

3. The case study

Fig. 1. Agricultural area lost to housing in the region of El Fath. Source: ETH Studio Basel, 2009 (Adapted by the authors).

and New Asyut city ‘‘NAc”, encompassing El Fath and Abnoub centers. The main challenges of the study area concerned the reduction of the negative effects of urban sprawl, and the development of effective strategies for the planning of new communities by studying the driving mechanisms of urban sprawl. Land Use and Land Cover ‘‘LULC” are shaped by the interactions of environmental, social, and economic forces. In order to better understand the effects of urbanization, this study examined and characterized LULC, focusing on the spatiotemporal differences of driving forces to develop a comprehensive identification of the causes and effects of the urbanization process. Several pertinent questions arose: How much had different driving forces contributed to changes in land use in the study area in the last 25 years? What was the relative importance of these driving forces? To what extent did the establishment of the new city affect the driving forces? The results of this study would help to identify the driving forces of LULC in the study area, which could then be used as a reference for other regions in Egypt to ensure that each region has differentiated and controlled planning for decision- making. The findings would be helpful to policy-makers to guide balanced urban growth and better future infrastructure planning, as well as to conserve natural resources, especially agricultural lands.

Asyut center and the area connecting it to New Asyut city were chosen for this study because of uncontrolled urban expansion in recent decades. Moreover, the urban growth pattern of this region had not been analyzed before. The study area comprised three administrative centers and a new city, viz. Asyut center with its capital city Asyut and 7 local units, El Fath center, its capital Elwasta, and 6 local units, Abnoub center, its capital, Abnoub city and 4 local units. Asyut city, one of the biggest and most important cities in Upper Egypt, is located on the west bank of the Nile River, 375 km south of Cairo (Figs. 2 and 3). It can be classified as multifunctional because it is the educational, medical, and commercial center of Upper Egypt. Asyut is a medium-sized city with a population of 509,156 (AGIC, 2016). As of 2015, the study area had a population of 1,732,931. Population statistics of the study area were available for 2015, 2004, 1996, and 1986. The population growth ratios in Asyut governorate from 1986 1996 and 1996– 2006 were 2.24% (AGIC, 2016) and 2.12% (calculated) respectively. The population in 1990 and 2003 was based on estimates (Table 1). The study area lies within the coordinates (Min X 300,495, Max X 341,745, Min Y 2,996,895, Max Y 3,026,805 –reference system UTM 36N-). 4. Methods The remote sensing technique was used to characterize the spatiotemporal trends of urban sprawl in the case study over two periods (1990–2003 and 2003–2015) stretching over 25 years. For temporal land use mapping of the study area, Landsat TM and ETM+ images for both the periods using multi-stage images were available. For the assessment of land cover change, Landsat imagery generated a time series of land cover data over the past 25 years that were used to quantify land use differences and to analyze urban sprawl and its causative factors. The long-term trends in the pattern of urban sprawl was used to determine its driving forces. The study used the logistic regression method (Sudhira et al., 2004) that effectively reflected the drivers’ spatial characteristics. Based on the study objectives, there were four categories of land use, viz. (i) urban areas which represent built-up areas consisting of

2. Background Monitoring systems such as remote sensing can be successfully utilized to study population dynamics and land use changes in densely populated urban areas, especially in developing countries like Egypt. Being cost-effective, remote sensing is increasingly used for the analysis of urban sprawl (Jat et al., 2008). In the literature, several modeling methods have been used to study changes in land use and cover. Spatial explicit modeling can explain the process of change quantitatively and facilitates understanding of the process. Previous studies have focused primarily on single large cities, with little exploration of the driving forces of changes in different towns in the same region (Shu et al., 2014). Detecting LULC changes and predicting likely changes for regions were attempted by Al Gharbiya governorate in North Egypt (Belal and Moghanm, 2011), and Daqahlia governorate (Hegazy and Kaloop, 2015). This study is the first in this region and in Egypt to derive and quantify the driving forces of LULC change to assess the relative importance of the driving forces in the establishment of a new city.

Fig. 2. The study area: three administration centers ‘‘Markaz”; Assiut, Abnoub, Elfath. Source: Google Earth, 2015, adapted by the authors.

61

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

The land use classification (urban, water, desert, agriculture) was revised and edited based on reliable data for the study area. Finally, classification accuracy was adopted to compare the classified images with the real status on the ground (Fig. 4). The accuracy of the system classification compared to the real LULC was 88% for the 1990 data; 93% for the 2003 data, and 84% for the 2015 data. 6. Results of the classification analysis

Fig. 3. Egypt Governorates map and the study area. Source: General Organization for Physical Planning, 2015, adapted by the authors.

residential, commercial, industrial complexes (Jat et al., 2008), (ii) water which represents the Nile river and the main canals, (iii) deserts which represent all arid areas east and west side of the Nile valley, (iv) agricultural lands which represent all cultivated areas and farms in the study area. To measure or estimate land use of the study area, remote sensing pattern recognition (supervised and unsupervised) methods were used (Castellana et al., 2007), and corroborated with the authors’ expert knowledge of the study area. 5. Spatiotemporal urban sprawl mapping Data for the study were collected from different sources. The satellite images of 1990, 2003, 2015 (Resolution 30 * 30 m) and Digital Elevation Model DEM were obtained from the Earthexplorer website (USGS, 2016). The road network was developed by the authors using the master plan of Asyut city and satellite images (Table 2).

In order to understand the changes in LULC, the gains and losses for each class were calculated. As shown in Table 3, Fig. 5, most land conversion in the first period (1990–2003) was attributed to the replacement of agricultural lands with urban areas. About 8428 acres had been changed to built-up areas, with 6125 acres (72.6%) being former agricultural lands (Fig. 5c). At the same time, 4235 acres of desert land were converted to farming. Hence, the net growth rate of built up area was 36.22%, and the net decrease in agricultural lands was 1.55% (Fig. 5a). For the period between 2003 and 2015 after the establishment of New Asyut city (NAc) in 2000, a rapid built-up development of over 12,496 acres was observed. Approximately 9020 acres (72.2%) of urban sprawl was observed in former agricultural lands (Fig. 5e). For the period 1990 to 2003, the population in the region grew by 30.3% while the built-up area increased by 38.47%, outstripping population growth. From 2003 to 2015, the rate of population growth was 24.6% while the built-up area grew by 36.24% (Fig. 5b). Hence, per capita consumption of land, i.e. the utilization of built up area for commercial, educational, residential, etc. per person, increased at a rate greater than that of population growth. On the other hand, a descriptive analysis showed a ‘‘big wave” of change in the agricultural areas vis-a-vis urban changes (Fig. 5f). The total agricultural land decreased only by 9.46% despite the urban sprawl. This was due to the creation of new agricultural land in the eastern desert area, next to the NAc. Hence, the increase in agricultural area more than compensated for the loss due to the urban sprawl, with increase of total agricultural areas by 3.75% (Fig. 5b). The changes in the acreage of water bodies were because of (i) the floods which submerged parts of the Nile islands, (ii) unauthorized piling in the river by local inhabitants, and (iii) the government Span project to generate energy and provide irrigation (Fig. 6).

Table 1 Population Growth Statistics of the Study Area centers and cities.

Asyut center El Fath center Abnoub center New Asyut city Total

Population 1990

Population 2003

Population 2015

Area in acres

Area in km2

576,489 145,305 189,616 – 911,410

774,525 231,323 301,863 – 1,307,711

1,007,332 302,997 392,602 30,000 1,732,931

53,897 38,094 46,293 32,800 171,083

218.11 154.16 187.34 132.74 692.35

Table 2 Data used in the study and their sources. Data

Source of Data

Function

Land Sat Image, April 2015

Landsat Archive, L8 OLI/TIRS, The United States Geological Survey ‘‘USGS” (USGS, 2016) Landsat Archive, L7 ETM + SLC (USGS, 2016) Landsat Archive, L4 5 TM (USGS, 2016)

To classify information of land use and land cover of the city and its variation through the study period

Digital Elevation Model Road Network

Aster Global DEM (USGS, 2016) Mapped manually using aerial photographs and image processing techniques.

To obtain higher-precision data for the land use classification analysis and to derive the driving forces

Detailed Land Use Map Reference data

Urban Planning Agency, Egypt Google Earth

To evaluate data precision and high-precision image sources in the study area

Land Sat Image, April 2003 Land Sat Image, April 1990

62

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

Fig. 4. Extracted land use maps.

Table 3 Urban land expansion in the study area from 1990 to 2015. Year

First study period 1990–2003 Area (acres)

Urban Desert Agriculture Water

Second study period 2003–2015 Change rate %

1990

2003

13,482.44 166,062.88 117,156.43 166,062

21,911.1 159,525.04 115,258.70 166,062

0 4.18 5.58 0

Unfortunately, even thirteen years after the establishment of a new community on the western bank of the Nile River, the government’s effort had not succeeded in curbing urban development encroaching on agricultural land. Nevertheless, there was some in reclaiming inexpensive new lands from the desert for farming. To better understand the transition potentials, the results were analyzed to extract the driving forces and their relative importance over a 25-year period, from 1990 until 2015.

Area (acres)

Change rate %

+

2003

2015

38.47 0.25 4.03 0

21,911.1 159,525.04 115,258.70 8185.13

34,084.06 143,009.5 120,280 7506.38

+ 0.22 11.05 9.46 17.95

36.46 0.58 13.21 10.54

7. Driving forces Driving forces are the forces that cause observed landscape changes, i.e. they are influential processes in the evolutionary trajectory of the landscape (Bürgi et al., 2004). Understanding the driving mechanisms of spatial expansion of urban areas is crucial for directing rational urban land expansion (Shu et al., 2014), just as identifying and quantifying the driving forces of LULC are

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

63

Fig. 5. Land change analysis in the study area in two periods 1990–2003 and 2003–2015.

Fig. 6. Spatiotemporal changes in the water body.

important for urban growth modeling, and for understanding the processes of land use transition. There is no known global formula for selecting land use drivers (Eyoh et al., 2012), as different criteria are applicable to developed and developing countries. Thus, a case by case approach should be adopted. Based on literature review (Alsharif and Pradhan, 2014; Eyoh et al., 2012; Schneeberger et al., 2007; Shu et al., 2014), information provided by the Urban Planning Agency and the results of a field survey, twelve salient explanatory variables were extracted and visualized. They consisted of the main variables, viz. natural eco-environment (spatial effects), land control policy, accessibility, and socio-economic neighborhood factors. Moreover, a direction trend was investigated through four axes. The ROC (Relative Operating Characteristic) test (Swets, 1986) was used to measure the goodness of fit of logistic regression. In our study, the initial result with a 10% sampling test, the ROC was 86%. Socio-economic factors also played an important role in driving LULC. In the traditional Egyptian community, families and friends tend to live close to one another. Urban development generally takes place in the vicinity of existing built up areas where essential infrastructure and amenities are readily available, unlike new or remote areas. In this study, five socio-economic factors were examined, viz. (i) distance to the center of the urbanized area (an updated image was used for each period) ‘‘Fdbu”, (ii) distance to the three cities (i.e. the capitals of the three administrative centers; Asyut, Abnoub, Alwasta)”Fdci”, (iii) distance to Asyut city which is the capital of the governorate ‘‘Fdas”, (iv) distance to NAc which encompassed most of the available land to build ‘‘Fdna”, and finally (v) distance to the water body ‘‘Fdw”. Accessibility is among the most important driving forces of LULC, including distance to major and minor roads ‘‘Fmr” and ‘‘Fmir”. Examination of the roads data of the study area showed

no significant changes as the urban sprawl was along existing roads. Hence, the same data were adopted for the entire period. The distance to the agricultural border (Fdbo) was another driving force considered in the study. Three constraints were selected as driving forces of the urban sprawl. They were constraints pertaining to height (Che), water bodies (Cw) and existing built up areas (Cbu). Finally, to investigate the trend of growth direction in the two periods (i.e. 1990–2003 and 2003–2015), four axes were used, viz. (i) East-west axis (X coo) ‘‘Fx”, (ii) North-south axis (Y coo) ‘‘Fy”, (iii) the Nile river axis (N axis) ‘‘Fni”, and (iv) the perpendicular axis to the Nile river towards NAc (NAc axis) ‘‘Fpni”.

8. Data processing and binary logistic regression Logistic regression can be used in modeling to explain the relationship of a number of independent variables (Xs) to a dichotomous single dependent variable (Y) that describes combined effects of several factors. The model gives the probability of LULC based on their driving factors, and quantifies the interaction between the different land use types and their drivers (Eyoh et al., 2012). Logistic regression has the advantage of exploring relationships between land conversion and causative factors quantitatively. Using the Vramer value, a test was conducted on the potential explanatory power of the proposed explanatory variables. The results of driving forces had ROC values of 86% and 84% for first and second study periods, respectively. The Pseudo R2 values obtained were all in the range of about 0.23, whereas a value greater than 0.2 is considered a relatively good fit (Clark and Hosking, 1986). This indicated that all the models performed well and the model variables could effectively interpret the LULC process. As shown in Table 4, most of the regression coefficients

64

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

Table 4 Summary of logistic regression models. Independent Variable

Intercept Height constraint Che Water body constraint Cw Existing built up areas constraints Cbu Distance to major roads Fmr Distance to minor roads Fmir Distance to built up area Fdbu Distance to the three cities Fdci Distance to Asyut city Fdac Distance to NAc Fdna Distance to water Fdw Distance to border Fdbo East West direction Fx North south direction Fy Direction along the Nile Fni Direction perpendicular to the Nile Fpni

Vramer’s V

Coefficient

Odds ratio

Rank

90–03

03–15

90–03

03–15

90–03

03–15

90–03

03–15

– 0.183 0.059 0.604 0.250 0.316 0.603 0.356 0.327 0.215 0.292 0.216 0.150 0.169 0.089 0.249

– 0.183 0.059 0.782 0.250 0.316 0.566 0.353 0.328 0.250 0.292 o.200 0.150 0.169 0.089 0.249

23.187 4.566 20.118 34.428 0.021 0.097 0.513 0.094 0.377 1.013 0.086 0.468 0.232 0.122 0.481 0.047

26.845 3.718 21.090 14.289 0.059 0.187 0.617 0.088 0.080 0.676 0.180 0.216 0.132 0.198 0.231 0.017

– – – – 0.979 0.907 0.598 0.910 0.685 0.363 0.917 0.626 1.261 0.885 1.617 1.048

– – – – 0.943 0.829 0.540 0.916 0.923 0.509 0.835 0.806 1.141 0.820 1.260 0.983

– a2 a3 a1 9 6 2 7 4 1 8 3 11 5 12 10

– a1 a3 a2 9 5 2 7 8 1 6 3 11 4 12 10

associated with the factors were negative. The output of the logistic regression model is a probability surface of urban development (Arsanjani et al., 2013). Each surface plot has a particular degree of probability (Fig. 7). The influences of the LULC factors during the two periods in the study area are shown in Table 4. The model illustrates that urban growth had been affected by the land control policy. Due to the establishment of NAc in 2000, new urban land development occurred mainly in this area. So, it seems that the largest urban growth process occurred there with coefficient 1.013 and odds ratio 0.363. The results indicated that the odds of the urbanization process in the area nearer to NAc were 1/0.363, i.e. 2.75 times as great as that of a region 1 km further away from this city. Despite the government providing most of the construction land at reasonable cost in NAc, the relative importance of this factor decreased to 0.676, and the odds ratio to 0.509 in the second period (2003– 2015). (The odds of urbanization in the area nearer to NAc decreased to 1.96 times as great as that of a region 1 km further away from this city). Due to a lack of economic opportunities, as well as shortage of educational and medical amenities in NAc, urban growth had not been followed by a corresponding rate of population increase; the population density in NAc was very low (226.8 people/km2). As indicated by the results of the study, the urban sprawl occurred mainly around existing urban areas where infrastructure and services were available. The distance to the agricultural border was also a significant driving force. Further urban expansion being planned around the existing built up areas caused further pressure on the edges of the new areas. Most informal sprawl occurred within the border area. However, there was a decrease of the informal sprawl in the second period. While the distance to Asyut city was more important than the distance to roads, it was less so in the second period. The decrease in the relative importance of some variables (Fdna, Fdbo, and Fdac) in the second period pointed to the importance of distance to major roads (Fmr), minor roads (Fmir), and to existing built up areas (Fdbu) (Figs. 8 and 9). The model demonstrated that minor roads had a bigger effect on urban development, as the probability ratio Fmir increased from 1/0.907 to 1/829. Strip and ribbon urban expansion patterns were detected along roads connecting villages in the study area. Generally, the limited influence of roads on urban land development in the study area was due to an imperfect road network. The decrease in the relative importance of distance to Asyut city might be due to the process of population densification in existing urban areas. As a result of political events coupled with significant rises in the price of land within the city, urban buildings began to increase in height (sometimes even in violation of the law), thus resulting in a higher population density in these areas.

The directional trend perpendicular to the Nile river towards NAc ‘‘Fpni” changed from 0.047 to 0.017. This finding was the opposite of what the government urban planning committee had intended. Despite efforts to direct the urban sprawl to areas outside the Nile valley, the informal sprawl was observed close to the river, and the fringes of the urban core. The impact of the other directions relative to the Nile were less distinct. These results show that the urbanization in the study area was likely to occur within the main existing urban cores.

9. Discussion This study is the first of its kind that investigated the spatiotemporal characteristics of national urban land dynamics in Upper Egypt. As previous studies in Egypt quantified the land use and land cover change, however they did not consider the urban driving forces effects (Rageh, 2007; Belal and Moghanm, 2011; Hegazy and Kaloop, 2015). A literature review for the recent global studies revealed that the dominant driving factors of high population countries -like -China- causing rapid urban expansion were population and economic growth, and national policies (Kuang et al., 2016), that’s like what this study has proved. Moreover, There are some similarities between the urban extension in Egyptian and Chinese cities. Both expand from urban to rural. As they have obvious urbanization patterns and rates at temporal scale (Kuang et al., 2014). In this study, All the driving force variables impacted LULC in the study area. However, their relative importance varied over the years mainly because of land control polices and socioeconomic conditions. The results showed that the attractiveness of the new city diminished with time. The rate of urban development outstripped the rate of population growth, with this problem being more severe in NAc. The 2015 population density in NAc was 226.8 people/km2, whereas in Asyut center it was 4618 people/ km2. This problem was due to government regulations in the new cities that required land owners to build on their lands within a specific time period. They were forced to build their homes, but because of the lack of accessibility and socio-economic problems, the people did not want to settle down in the new city. There was more construction around existing urban areas and along the roads as the population continued to increase in the second time period (2003–2015). That explained the increased relative importance of distance to built-up areas, distance to major roads, and distance to minor roads in the second period of the study. These results showed there was an expansion of the strip and ribbon urban expansion patterns, thus increasing the risk of the sprawl onto existing agricultural lands.

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

Fig. 7. Independent variables in the study area for the period 1990–2003. These images were updated in the second period 2003–2015.

Fig. 8. The driving forces coefficient change.

Fig. 9. The probability of transition potential in 2003–2015.

65

66

H. Mahmoud, P. Divigalpitiya / Egypt. J. Remote Sensing Space Sci. 22 (2019) 59–66

Land control policy factors and accessibility factors had significant impact on LULC. Governmental policies could control and redraw urban development by absorbing urban development partly in areas outside the Nile valley and also by encouraging agricultural development in that direction. While the governmental managed to achieve some degree of success in controlling the urban sprawl, the lack of integration between land policies and the socioeconomic needs of the citizens resulted in new settlers heading back into the valley again. In urban planning, the driving forces of the sprawl should be identified, quantified, and grasped comprehensively. It is necessary to strengthen the scientific basis of new cities planning and more attention should be given to devising long-term strategies to curb disorderly urban sprawl. Government policies could impact most urban sprawl driving forces, either by stimulating or constraining them. Hence, it is important to understand these driving forces before formulating policies to help the government curb future urban sprawl. 10. Conclusion Quantifying LULC differences over a span of 25 years (1990– 2003 and 2003–2015) revealed that urban growth, especially on agricultural lands, had increased rapidly. In the first period, i.e. 1990–2003, governmental policies of urban development and agricultural development managed to create a balance in land use. The land control policy played a significant role in shaping urban development in the second stage. Establishing New Asyut city initially attracted urban development quickly, but the lack of supportive government policies and inattention to the socioeconomic factors made the new city less attractive to reside in. Moreover, infrastructure accessibility such as distance to major and minor roads, and socioeconomic factors were important driving forces of the increase of built-up areas, and the emergence of strip and ribbon urban expansion patterns. Left unchecked, this disturbing trend could contribute to the depletion of existing agricultural lands. Directing urban development out of the Nile valley is a crucial economic issue as well as an environmental issue. Thus far, government policies using conventional methods have not succeeded in curbing the urban sprawl. Management policies based on more scientific analysis of spatiotemporal variation could be more effective. Pragmatic policies to improve the socioeconomic conditions in NAc are also required. Moreover, the government has to deal with the informal sprawl along with roads and around existing built-up areas. In this first study on Upper Egypt, a land control policy factor, two accessibility factors, five socioeconomic factors, four directions factors, and three constraints were selected as driving forces of the urban sprawl. All these land use variables contributed to the urban expansion of study area. Nevertheless, further research is recommended to gather more data, especially historical data, to add to the selection of factors. It would also be useful to examine multiple scenarios to predict future LULC. Conflict of interest The authors declared that there is no conflict of interest.

References Aafify, A., 1999. The Philosophy of new towns and their evolution – an analytical study for theory and practice. Seminar: New Towns in the Arab World and their role in Sustainable Development. Ibn Zohr University, Agadeer, Morocco. Alsharif, A., Pradhan, B., 2014. Urban sprawl analysis of Tripoli Metropolitan City (Libya) using remote sensing data and multivariate logistic regression model. J. Indian Soc. Remote Sens. 42 (1), 149–163. https://doi.org/10.1007/s12524-0130299-7. Arsanjani, J.A., Helbich, M., Kainz, W., Boloorani, A.D., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. Appl. Earth Observ. Geoinf. 21, 265–275. www.elsevier.com/ locate/jag. AGIC, Asyut Governorate Information Center, (2016). Belal, A., Moghanm, F., 2011. Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya govenorate, Egypt. Egypt. J. Remote Sens. Space Sci. www.elsevier.com/locate/ejrs. Bürgi, M., Hersperger, A.M., Schneeberger, N., 2004. Driving forces of landscape change – current and new directions. J. Landscape Ecol. 19, 857–868. Castellana, L., D’Addabbo, A., Pasquaiello, G., 2007. A composed supervised/ unsupervised approach to improve change detection from remote sensing. Pattern Recogn. Lett. 28, 405–413. https://doi.org/10.1016/j.patrec.2006.08.010. Clark, W.A., Hosking, P.L., 1986. Statistical Methods for Geographers (Chapter 13). John Wiley & Sons, New York. ETH Studio Basel, Contemporary City Institute (2009). Nile Valley: Urbanization of limited resources, Federal Institute of Technology Zurich (ETH), Book ISPN 9783-909386-32-1. Eyoh, A., Olayinka, D.N., Nwilo, P., Okwuashi, O., Isong, M., Udoudo, D., 2012. Modelling and predicting future urban expansion of lagos, Nigeria from remote sensing data using logistic regression and GIS. Int. J. Appl. Sci. Technol. 2, 116– 124. Hegazi, I.R., Moustafa, W.S., 2013. Toward revitalization of new towns in Egypt case study: Sixth of October. Int. J. Sustainable Built Environ. 2 (1), 10–18. Hegazy, I.R., Kaloop, M.E., 2015. Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int. J. Sustainable Built Environ. 4, 117–124. www.sciencedirect.com. Jat, M.K., Garg, P.K., Khare, D., 2008. Monitoring and modeling of urban sprawl using remote sensing and GIS techniques. Int. J. Appl. Earth Observ. Geoinf. 10, 26–43. www.elsevier.com/locate/jag. Kuang, W., Chi, W., Lu, D., Dou, Y., 2014. A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. J. Landscape Urban Plann. 132, 121–135. Retrieved from www.elsevier.com/ locate/landurbplan. Kuang, W., Liu, J., Dong, J., Chi, W., Zhang, C., 2016. The rapid and massive urban and industrial land expansions in China between 1990 and 2010: A CLUD-based analysis of their trajectories, patterns, and drivers. J. Landscape Urban Plann. 145, 21–33. Madboly, M., 2005. Sustainable development of the Egyptian countryside through strategic planning of the Egyptian villages. The proceeding of the Arab Regional Conference: The interdependence between the rural and the urban. The General Organization of Urban Planning GOPP, the Academic Library, Cairo, Egypt. Rageh, A., 2007. The Egyptian urban: monitor the development of Egyptian urban in the late of twentieth century and survey its future tracks until 2020. The proceeding of the Arab Regional Conference: The interdependence between the rural and the urban. The General Organization of Urban Planning GOPP, the Academic library, Cairo, Egypt. Schneeberger, N., Burgi, M., Hersperger, A.M., Ewald, K.C., 2007. Driving forces and rates of landscape change as a promising combination for landscape change research—An application on the northern fringe of the Swiss Alps. J. Land Use Policy, 24, pp. 349–361. www.elsevier.com/locate/landusepol. Shu, B., Ahang, H., Li, Y., Qu, Y., Chen, L., 2014. Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang, China. J. Habitat Int. 43, 181–190. www. elsevier.com/locate/habitatint. Sudhira, H.S., Ramachandra, T.V., Jagadish, K.S., 2004. Urban sprawl: metrics, dynamics and modelling using GIS. Int. J. Appl. Earth Observ. Geoinf. 5, 29–39. https://doi.org/10.1016/j.jag.2003.08.002. Swets, J.A., 1986. Indices of discrimination or diagnostic accuracy: their ROC’s and implied models. Psychol. Bull. 99, 100–117. https://doi.org/10.1037/00332909.99.1.100. USGS, 2016. The United States Geological Survey, http://earthexplorer.usgs.gov/ Websites: 1) USGS, United States Geological Survey (2016). http:// earthexplorer.usgs.gov/.