Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data

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1 May, 2015 Journal of Resources and Ecology Vol.6 o.3 J. Resour. Ecol (3) DOI: /j.issn x Article Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data LI Qiuying 1, 2, FAG Chuanglin 1 *, LI Guangdong 1, 2 and RE Zhoupeng 1, 2 1 Institute of Geographic Sciences and ational Resources Research, CAS, Beijing , China; 2 University of Chinese Academy of Sciences, Beijing , China Abstract: Qingdao is one of the essential growth poles in the process of new-type urbanization in Shandong Province. The study on the relationship between urban expansion and driving factors in this area is representative. This paper examined urban expansion from the perspective of non-urban to urban conversion, detailing an empirical investigation into the spatiotemporal variations and impact factors of urban expansion in Qingdao. By using the Urban Expansion Intensity Index (UEII) and Urban Expansion Differentiation Index (UEDI), the spatial and temporal difference of urban expansion in the Municipal District, Jiaozhou County, Jimo County, Pingdu County, Jiaonan County and Laixi County were calculated on a county unit data set for the period 1990 to A GIS and logistical regression models were applied for discussing the results of various factors in land use change. Results indicated that the elevation and slope factors showed negative effects to urban expansion. Distance to the city center and to road both also conferred negative effects. The population density and GDP were vital and positive factors of urban conversion. eighborhood factors showed consistently positive effects. The magnitude of factors was various in different counties. A better understanding of the factors influencing land use change could support land use management and planning decisions. Key words: urban expansion; driving factors; logistic regression model; Qingdao City 1 Introduction At present, half of the world s population dwells in urban areas, and it is an approximately calculated that this will increase to 67.2% by 2050 (ations 2012). The growth of urban land areas is a worldwide phenomenon; unparalleled urban growth rates can be found in all urban area in China over the last two decades (Zhan et al. 2013). In 1990, the total urban area was , which rapidly increased to in 2008 in China. As urban areas expand into the surrounding rural landscapes, adverse impacts have occurred in the environment at multiple spatial and temporal scales, including: climate change and global warming (Deng et al. 2013; Franklin et al. 2014; Jones and Somper 2014); fragile ecosystems of soil erosion and desertification (Deng and Bai 2014); lack of natural resources; biodiversity and wild animal loss (McKinney 2006; McKinney 2008). Understanding and characterizing the changing patterns of urban expansion are important; urbanization will be conducted to make policy in the foreseeable future (Deng et al. 2012). The growth of urban land areas occurs as the results of several factors: global economic activity, demographic factors, natural factors, local and neighborhood factors (Wu et al. 2002; Wu and Zhang 2012). The classic von Thümen system of land use (1875) interprets that urban areas are influence by transportation costs. The spatial arrangement of urban expansion is the consequence of socioeconomic processes and interactions (Crk et al. 2009). The form of urban and rural land uses makes a vivid reflection of different topographic, political, socioeconomic, neighborhood factors, all of which affect land use change (Li et al. 2013). In previous work, the unit of study was mainly by province unit, lacking the county unit evaluation. How can we scientifically quantify and describe changes Received: Accepted: Foundation: The Ministry of Land and Resources of Public Welfare Scientific Research (o ). * Corresponding author: FAG Chuanglin. fangcl@igsnrr.ac.cn.

2 LI Qiuying, et al.: Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data 173 in urban land-use? A wide range of models have been used to estimate the influence factors, including multiple regression (Lichstein et al. 2002; McMahon and Diez 2007), cellular automata (CA) (Li and Yeh 2001), Dynamics of Land Systems (DLS) (Deng et al. 2008) and logistic regression (Getis and Griffith 2002; Wu et al. 2009). In spite of the successful verification by those models, the models are also restricted in certain features. Multiple regressions require conditions such as independence of variables and model linearity (Omar et al. 2014). However, the data on land use change are inconsistent with these assumptions. The CA model is capable of describing an environment in which the interactions occurring among individuals in grid cells of various land uses. However, CA models hardly interpret the spatial-temporal process of land use change (Chen et al. 2002). The DLS model is an effective simulation model for integrating multiple data sources to simulate the dynamics of land system. The main limitation of DLS is a lack of prior knowledge on land conversion. However, logistic regression has a strong capacity for interpreting land use change (Huang et al. 2009), making this an excellent tool to explore the impacts of explanatory variables on the probability of land use change. The city of Qingdao is located in the east of China in the Shandong Province which is a coastal province; it plays an important role in China s economy pattern. As one of the essential growth poles in the process of urbanization in Shandong, the study of the relationship between urban expansion and the driving factors in this area is representative of other patterns of urbanization seen elsewhere. This paper aims to provide policy recommendations for the healthy development of urbanization in China. This paper presents the methods and results of the spatial and temporal patterns of urban land-use changes in Qingdao by county from 1990 to The UEII and UEDI were used to calculate the temporal and spatial differences of urban expansion. As the driving factors of urban development, nine variables of topographic, socioeconomic and neighborhood factors were chosen for analysis in this study. A logistical regression model was adopted to analyze the role of spatial factors on urban expansion. 2 Methods 2.1 Study areas Qingdao city (between E and between ) is located in the south of Shandong peninsula urban agglomeration, with a total area of approximately ; roughly 37.7% is flat, 21.7% is basin, 25.1% is hill and 15.5% is mountainous. It is a coastal city on the Huanghai Sea with the coastline The mountainous areas are primarily located in the east, south and north, with an average elevation ranging from m, while the plains lie in the center, at an elevation of 100 to 400 m (Fig. 1). Qingdao is composed of the Municipal District ( ), and Jiaozhou ( ), Jimo ( ), Pingdu ( ), Jiaonan ( ), and Laixi ( ). In terms of area, Pingdu is the biggest county in Shandong Province Data processing The data used in this study included land use and terrain data, demographic data and transportation network data over the period of The land use data were generated from the Data Center for Resources and Environment Sciences (Chinese Academy of Sciences, CAS). After being preprocessed in the ArcGIS 9.3, according to national land use classification criteria, the land use types can be divided into two categories of Qingdao City in the years of 1990, 2000 and 2008 (Fig. 2). Fig. 3 showed the urban expansion of Qingdao from 1990 to All land use files in vector format were rasterized at a resolution of 100 m 100 m. This resolution was fine enough for eliminating positional errors reported in the data. 2.3 Quantifying the spatial and temporal urban expansion Urban Expansion Intensity Index (UEII) The Urban Expansion Intensity Index (UEII) represents the ratio of urban expansion area accounting for the total area Shandong Province Qingdao Low: : Municipal District; 2: Jiaozhou; 3: Jimo; 4: Pingdu; 5: Jiaonan; and 6: Laixi Fig. 1 Location of the studied area and its topography Elevation (m) Value High: 1209

3 174 Journal of Resources and Ecology Vol.6 o.3, 2015 Urban on urban Urban on urban Urban on urban Fig. 2 Urban expansion of Qingdao in 1990, 2000 and in a unit. The index standardizes the growth rate of urban land and makes the results comparable in temporal sets. The formula is: ULAit ULA 2 it1 UEIIi = 100 (1) TLA t Pingdu City Center Jiaozhou City Center Jiaonan City Center i Laixi City Center Jimo City Center Qingdao City Center Fig. 3 Urban expansion in Qingdao from 1990 to City center Urban changed UEII i is the urban expansion intensity index in i unit, ULAit 2 and ULAit is the area in i unit at times t 1 2 and t 1, respectively. TLA i is the total area in i unit, t is the time span of the study Urban Expansion Differentiation Index (UEDI) The Urban Expansion Differentiation Index (UEDI) calculates the proportion of the increased urban area in a unit in proportion to the total changed area. This index makes the growth of urban land expansion comparable in spatial units and could analyze the differences in regions. The formula is: ULAit ULA 2 it ULA 1 t UEDIi = 100 ULA ULA ULA t2 t1 it1 UEDI i is the urban expansion differentiation index in i unit, ULAit 2 and ULAit are the total area in i unit at times t 1 2 and t 1, respectively. 2.4 Logistic regression To analyze the spatial patterns of urban expansion, this study employed logistical regression models. Spatial sampling was used to reduce spatial dependence (Dubovyk et al. 2011). A suitable sampling window was chosen after comparing different sampling window sizes. Logistic regression is capable of modeling the impacts of dependent variables. In this paper, a binary logistic regression was used to investigate the effects of topographic, socioeconomic, proximity, and neighborhood variables on the urban growth probability map. The essence of the land use change of a cell unit was taken as binary: (2)

4 LI Qiuying, et al.: Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data 175 either urban transition or no transition. A binary value of 1 means the areas were likely converted to urban land while 0 means no transition. The Percent Correct Predictions (PCP) was applied to estimate the accuracy of the model. The logistic regression model expression is as follows: y log( ) = β0 + βx+ ε (3) 1 y where y is the probability of conversion identifying urban land, β 0 is the intercept, parameter β is the estimated coefficients of x, ε is a randomly distributed residual error. The heterogeneity of spatial data needs to be considered when using logistic regression (Getis and Griffith 2002). Spatial autocorrelation refers to the lack of randomness of a variable value because of spatial structuring. To eliminate the inaccuracy of parameter estimation, spatial dependence should be tackled (Long et al. 2012). A cell in land use always has related with each other. However, an econometric model has the hypothesis that each observation should have no error terms for each observation. Therefore, the data should be filtered to remove any geographic autocorrelation efficiently (Munroe et al. 2004). This study used regular sampling methods to verify the proximity of observations. A check of joins was checked each time by contrasting land use change in adjacent cells. The number of joins was related to the window size. A large sampling window led to the loss of certain information, while a small sampling window was insufficient for filtering out geographic autocorrelation. After verification, a sampling window of 3 3 (300 m 300 m) was perfect. 2.5 Variables Many types of driving factors have been applied to the studies of urban expansion (Deng et al. 2008). In this study, nine variables have been chosen to analyze topographic, socioeconomic, proximity and regional factors. Topography variables: topography affected the extent and spatial patterns of the city by restricting the water resources and land provisions. Urban expansion is less likely to occur in rugged terrain. Therefore, two topographic variables were included in the analysis: elevation and slope. Both elevation and slope were derived from the 90m 90m SRTM elevation data from the USGS to calculate slope. Socioeconomic variables: in this study, the socioeconomic factors of population density and Gross Domestic Product (GDP) were analyzed. Typically, population density is one of the crucial criteria for urbanization research because population density significantly impacts urban expansion. GDP was also considered as a chief factor, because GDP also has shown crucially positive effects on urban expansion. Proximity variables: there is a city center where the local government is located in each county (Municipal District, Jiaozhou, Jimo, Pingdu, Jiaonan, and Laixi). The probability of being urbanized in a panel is more likely if it close to Table 1 Influencing factors of urban land conversion in the case study of Qingdao. Classification Factors Definitions Topographic Elevation Digital Elevation Model variables Slope Terrain slope derived from DEM Socioeconomic GDP GDP variables Population population Proximity variables eighborhood variable Dis2Citycenter Distance to county center Dis2Railway Distance to railway Dis2Majorroad Distance to highway, national and provincial roads Dis2Cityroad Distance to city roads eighborhood Proportion of urban area within a 300 m window the city center. Roads play an important role in urban land conversion because roads can facilitate residents work and daily life. In our study, three types of roads were considered, railway, major roads and city roads. Major roads included highway, national roads and provincial roads. City roads mainly connected to downtown traffics. The data were derived from Data Center for Resources and Environment Sciences (Chinese Academy of Sciences, CAS). Proximity to the city center and roads were calculated as the Euclidean nearest distance using Spatial Analyst in ArcGIS 9.3. eighborhood variable: the proportion of urban land is considered as a variable. We calculate the presence of urban land within a 3 3 pixel window (approximately 150 m radius) to diagnose the effects of neighborhood factors. All of selected variables were compiled in raster files with a spatial resolution of 100m for formulating a map. There were a number of pixels ( ) for dependent variables in this study (Table 1). 3 Results 3.1 Spatial and temporal analysis of urban land conversion in Qingdao The temporal characteristics of Qingdao from 1990 to 2008 The results of the UEII are shown in Table 2. UEII demonstrated that the urban area has expanded rapidly since In 1990, the urban area was 150 2, it rose to in 2000, and it reached up in The UEII index is and in and , respectively. This illustrated that the urban land expansion sped up from The standard deviation index was and in two stamps which reflected the obvious spatial differentiation patterns among the counties. The urban area of the Municipal District reached an area of in 2008, which accounts for 55.73% of the total urban land in Qingdao.

5 176 Journal of Resources and Ecology Vol.6 o.3, 2015 Table 2 The UEII and UEDI index in Qingdao from 1990 to Index UEII UEDI District Municiple District Jiaozhou Jimo Pingdu Jiaonan Laixi Qingdao S.D Table 3 Logistic regression results of variables. Variable Municipal District Jiaozhou Jimo Pinggu Jiaonan Laixi Elevation * ** ** ** ** Slope * ** ** ** ** GDP * * * Population * * * Dis2Citycenter * * * Dis2Railway () * * Dis2Majoroad () * * * Dis2Cityroad () * * * eighborhood ** ** ** ** ** ** n log likelihood P value PCP Moran s I 0.05** 0.03* 0.02** 0.05* ** *: P<0.05,**: P< The spatial characteristics of Qingdao from 1990 to 2008 UEDI was used to compare the expansion differences in various counties. According to the growth rate of urban expansion, the counties were divided into three categories: high-speed expansion, medium-speed expansion and lowspeed expansion. High-speed expansion referred to the speed of urban expansion in a county faster than the average speed. The county of an average rate of urban expansion was in the medium-speed expansion category. The lowspeed expansion category was slower than the average rate of urban expansion. The urban expansion rate of Jiaonan and Jimo ranked as high-speed expansion category, with a UEDI of and , respectively. The urban expansion rates of Pingdu and the Municipal District were in the medium-speed expansion category with a UEDI of and , respectively. Laixi and Jiaozhou were both in the third low-speed expansion category, with a UEDI of and , respectively. 3.2 Quantitative analysis of mechanisms of urban land transition The logistical regression models explained the variance in the urban expansion in these six counties using nine variables. As can be seen from Table 3, the regression models were estimated using the maximum likelihood algorithm. All the models are significant, each with a p value of The overall percentages of correctness values from high to low were (Jiaonan), (Laixi), (Municipal District), (Jiaozhou), (Jimo) and (Pingdu). All the variables including topographic, socioeconomic, proximity and neighborhood factors together had an important relationship with the urban expansion of the six counties. Elevation and slope conferred negative effects, as did the distance to the city center and the distance to three types of road network showed negative effects. Meanwhile other variables including GDP, population and neighborhood factors showed consistently positive effects (Fig. 4).

6 LI Qiuying, et al.: Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data 177 a b c d e Elevation High:1209 Low:115 Slope Dis2Citycenter High: High:50001 Dis2Railway High:31224 Dis2Majorroad High:46390 f g h i Dis2Cityroad High:15848 GDP High:95223 Low:73 Population High:10834 eighborhood High:9 Fig. 4 Spatial distribution of driving factors. (a) Elevation; (b) Slope; (c) Dis2Citycenter; (d) Dis2Railway; (e) Dis2Majorroad; (f) Dis2Cityroad; (g) GDP; (h) Population; and (i) eighborhood. 4 Discussion Topographic factors: Elevation and slope conferred negative effects on urban expansion. Urban conversion of steep and elevated parcels costs more than construction in flat areas. The topographic factor had a relatively important and negative effect in Pingdu County s urban expansion, because the elevation was higher than in other counties, with the exception of the Municipal District. Although the elevation of the Municipal District was the highest, many wealthy people could afford the construction costs in order to enjoy the amenities afforded by open space. Generally, the coefficients of the topographic factors were not as great of an effect compared to the other factors analyzed. Socioeconomic factors: Population density was the one of the most vital factors. The coefficients for each county were relatively higher for population compared to other factors. In 2008, the urban population raised 2.76 million (Municipal District), 0.36 million (Jimo), 0.33 million (Jiaonan), 0.25 million (Laixi), 0.25 million (Pingdu) and 0.23 (Jiaozhou), respectively. The parcels in these regions had more opportunity to convert to urban land due to increases in the population from in-migration of urban residents that raised the demand for housing in the area. With the increased urbanization and industrialization, farmers had a strong desire to find a job and make a living in the urban area. Proximity factors: The GDP coefficients were in Laixi, in Jiaonan, in the Municipal District, in Jimo, in Jiaozhou, and in Pingdu. The actual value of the GDP was 3.3 million (Laixi), 4.3 million (Jiaonan), billion (Municipal District), 4.9 million (Jimo), 4.7 million (Jiaozhou), and 4.1 million (Pingdu), respectively. This indicates that while generally positive, the relative impact of GDP changed depending on the county, likely as a result of other factors. Generally, GDP had positive effects on the conversion to urban area. The economy was rapidly developed from 1990 to 2008, and this industrialization is known to be one of the main driving forces of urban expansion. Economic development relied on the construction of infrastructure, including transportation, telecommunications and utility services, all of which required abundant industrial land provisions. The expansion of the urban area accompanied by the economy rapidly developing during which, urban land tended to be used for infrastructure adjustments which in turn supported the steady growth of the economy. Economic growth is the one of the main driving factors of urban conversion. The distance to the city center showed consistently negative effects on urban expansion in the six cities. Logically, development declined with as the distance from an urban center increased, due to a lack of access to

7 178 Journal of Resources and Ecology Vol.6 o.3, 2015 service provisions or other urban benefits. Interestingly, the coefficient was in Pingdu, which illustrated that the magnitude of the effect of the distance to the city center was not as important in this county when compared to other counties. This finding may be due to the fact that Pingdu is the largest city of Qingdao and Shandong, with a total area of , therefore urban expansion of this city dispersed over this large space, reducing the relative negative impact of the distance to the city center. The distance to roads showed negative effects, likely due to commuting and other transportation costs. As observed in the research, the importance of the distance to railway, distance to the highway, national and provincial roads were relatively less important than the distance to city roads. Generally, the railway and highway, national and provincial roads were evenly distributed across each county. eighborhood factors: At the neighborhood level, neighborhoods with a 300m window played a positive role in the spatial pattern of development. The urban conversion was more likely to take place in locations near the urban area. This was accordance with the first law in geography: the proximity effects. The coefficients of Jiaozhou and the Municipal District were relatively high, with the urban expansion more concentrated around the urban area than in other counties. In these cases, the expansion had less cost in terms of the service provision and other benefits. The coefficient of Jiaozhou was the lowest, We found that the Industrial Development Zone had been built after 2000 as a new industrial zone for Qingdao according to a government plan. The zone was 10 far from the urban center. 5 Conclusion The discussion in the preceding sections has directed the attention of the reader to the magnitude and driving factors of urban expansion of Qingdao in each county unit for the past two decades. The urban land increased by 219% from in 1990 to in The UEII and UEDI were used to calculate the temporal and spatial differences of urban expansion in each region. These indices indicated that urban land expansion sped up in the time period. Urban land expanded more strongly in Jiaonan and Jimo counties. Topographic, socioeconomic, proximity and neighborhood factors consistently played an important role on urban expansion. Interestingly, the magnitude of impact of each of the analyzed factors varied in each of the six counties. The relationship between the urban conversion and influencing factors were explored using logistic regression using multiple binary regression models with appropriate sampling to create a new model to support change analysis. The model indicated that both elevation and slope showed negative effects to urban expansion. Distance to the city center and to road both also conferred negative effects. The population density and GDP were vital and positive factors of urban conversion. eighborhood factors showed consistently positive effects. As expected, economic growth is one of the main driving factors of urban conversion. A better understanding of the factors influencing land use change could support land use management and planning decisions. This study attempts to apply a logistical regression model to analyze the factors affecting urban land use. The case study of Qingdao clearly demonstrated that the logistical regression model is a useful tool in simulating urban conversion. 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8 LI Qiuying, et al.: Quantitative Measurement of Urban Expansion and Its Driving Factors in Qingdao: An Empirical Analysis Based on County Unit Data 179 McKinney M L Effects of urbanization on species richness: A review of plants and animals. Urban Ecosystems, 11(2): McMahon S M and J M Diez Scales of association: Hierarchical linear models and the measurement of ecological systems. Ecology Letters, 10(6): Munroe D K, J Southworth and C M Tucker Modeling spatially and temporally complex land-cover change: The case of western Honduras. Professional Geographer, 56(4): ations U World Urbanization Prospects, the 2011 Revision. esa.un.org/unpd/wup/ Omar Q, M S S Ahamad, W M A W Hussin, et al Markov CA, multi regression, and multiple decision making for modeling historical changes in Kirkuk City, Iraq. Journal of the Indian Society of Remote Sensing, 42(1): Wu B, Huang B and Fung T Projection of land use change patterns using Kernel logistic regression. Photogrammetric Engineering and Remote Sensing, 75(8): Wu J G, Shen W J, Sun W Z, et al Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecology, 17(8): Wu K Y and Zhang H Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing Hangzhou metropolitan area, eastern China ( ). Applied Geography, 34: Zhan J Y, Huang J, Zhao T, et al Modeling the impacts of urbanization on regional climate change: A case study in the Beijing- Tianjin-Tangshan Metropolitan Area. Advances in Meteorology, (Special Issue): 青岛市城市用地扩展与影响机制分析 基于县域数据的实证分析 李秋颖 1,2, 方创琳 1, 李广东 1,2 1,2, 任周鹏 1 中国科学院地理科学与资源研究所, 北京 ; 2 中国科学院大学, 北京 摘要 : 青岛市是山东省经济发展和新型城镇化进程中的重要增长极, 因此, 研究其城市扩展与影响因素的过程有重要的意义 本研究从非城镇建设用地 城镇建设用地转换的视角, 对青岛市城市用地扩展的时空格局与影响机制进行定量实证分析 采用城市扩展强度指数和城市扩展差异指数分析了青岛市辖区及胶州 即墨 平度 胶南和莱西五个县级市 1990 年至 2008 年城市用地扩展的时空格局差异 结合 GIS 和逻辑回归模型以县域单元数据为基础分析了用地扩展的影响机制 结果表明, 高程 坡度 到道路的距离和到市中心的距离对城市扩展有显著负相关作用 ; 而人口和 GDP 对城市扩展具有正相关作用且影响程度高, 邻域因素也具有正相关作用 作用力在不同的县域影响范围有所不同 城市用地扩展的影响因素分析为土地利用管理和土地利用规划有较强的指导意义 关键词 : 城市用地扩展 ; 影响因素 ; 逻辑回归模型 ; 青岛