1# RECORD e13b244754a8414a178e49afae995b23f640983e2# TITLE: A GIS-based spatial analysis of housing price and road density in proximity to urban lakes in Wuhan City, China3# YEAR: 2015.0 VENUE: Chinese Geographical Science DOI: 10.1007/s11769-015-0788-44# AUTHORS: Zuo Zhang; Shukui Tan; Wenwu Tang5# PAGES: 16 CANDIDATE TABLE PAGES (1-based): [10]6# ABSTRACT: As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different from hedonic approach employed in existing researches on urban open spaces in China, this paper integrates housing price surface with road density to analyze the spatial characteristics in proximity to urban lakes in Wuhan City, China. With the expansion of Wuhan City, urban lakes became polluted, they shrunk or even disappeared, leading to unfavorable conditions for sustainable development of the city. To better understand the spatial relationship between the city and lakes, we classify the urban lakes in Wuhan central area into ′lakes in the urban center′ and ′lakes in urban fringe′. Based on housing price surface we explore the spatial characteristics in proximity to different lakes and differences between the lakes. We also use Geographic Information System (GIS) tool to calculate road density as a supplementary indicator to reflect the accessibility in proximity to urban lakes. The results indicate that relative independence exists between different towns, and the spatial characteristics are different depending on scales and locations. In most of cases, the road density is lower where closer to the lakeshore while the housing price exhibits an opposite pattern. We conclude that city governments and urban planners should give more considerations to these spatial differences, somewhere should be better planned and protected as an important waterfront and somewhere the control of unreasonable real estate development nearby should be strengthened.789===== PAGE 1 (score 11, numeric-paren 0, table-words 0, var-words 11) =====1011 12Chin. Geogra. Sci. 2015 Vol. 25 No. 6 pp. 775–790 13 14 Springer 15 Science Press 16doi: 10.1007/s11769-015-0788-4 17www.springerlink.com/content/1002-0063 18 19Received date: 2015-04-01; accepted date: 2015-08-04 20Foundation item: Under the auspices of National Natural Science Foundation of China (No. 41201164, L1422012), Humanity and Social 21Science Youth Foundation of Ministry of Education of China (No. 12YJCZH299), China Postdoctoral Science Foundation (No. 222012M521420, 2014T70693) 23Corresponding author: TAN Shukui. E-mail: tansk@126.com 24© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag Berlin Heidelberg 2015 25A GIS-based Spatial Analysis of Housing Price and Road Density in 26Proximity to Urban Lakes in Wuhan City, China 27ZHANG Zuo1, 2, TAN Shukui1, TANG Wenwu3 28(1. College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China; 2. School of Business, 29Hubei University, Wuhan 430062, China; 3. Department of Geography and Earth Sciences and Center for Applied GIScience, Univer-30sity of North Carolina at Charlotte, Charlotte 28223, U.S.A.) 31Abstract: As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different 32from hedonic approach employed in existing researches on urban open spaces in China, this paper integrates housing price surface with 33road density to analyze the spatial characteristics in proximity to urban lakes in Wuhan City, China. With the expansion of Wuhan City, 34urban lakes became polluted, they shrunk or even disappeared, leading to unfavorable conditions for sustainable development of the city. 35To better understand the spatial relationship between the city and lakes, we classify the urban lakes in Wuhan central area into ′lakes in 36the urban center′ and ′lakes in urban fringe′. Based on housing price surface we explore the spatial characteristics in proximity to differ-37ent lakes and differences between the lakes. We also use Geographic Information System (GIS) tool to calculate road density as a sup-38plementary indicator to reflect the accessibility in proximity to urban lakes. The results indicate that relative independence exists be-39tween different towns, and the spatial characteristics are different depending on scales and locations. In most of cases, the road density is 40lower where closer to the lakeshore while the housing price exhibits an opposite pattern. We conclude that city governments and urban 41planners should give more considerations to these spatial differences, somewhere should be better planned and protected as an important 42waterfront and somewhere the control of unreasonable real estate development nearby should be strengthened. 43Keywords: urban lakes; housing prices; road density; Geographic Information System (GIS); Wuhan City, China 44 45Citation: Zhang Zuo, Tan Shukui, Tang Wenwu, 2015. A GIS-based spatial analysis of housing price and road density in proximity to46urban lakes in Wuhan City, China. Chinese Geographical Science, 25(6): 775–790. doi: 10.1007/s11769-015-0788-4 47 48 49 50 511 Introduction 52Urban open space preserves are usually indispensable to 53the area sustainability, being an essential element of 54land use policy in rapidly urbanizing areas (Esbah et al., 552009; Lichtenberg, 2011). However, with the rapid ur-56ban expansion, most local governments do not estimate 57carefully the benefits of such open spaces, and real es-58tate developers often fail to consider all public infra-59structure costs generated by their projects (Brueckner, 602000). Constant increase in urban sprawl led to loss of 61open spaces (Smith et al., 2002). Some studies show 62that open space preserving expands the urban bounda-63ries and creates leapfrog development (Irwin and 64Bockstael, 2004). Other studies found that rapid urbani-65zation threatens the availability and accessibility of ur-66ban open spaces, but there is no clear conflict between 67open space preservations and urban spatial expansions 68(Lichtenberg and Hardie, 2007). Furthermore, as a basic 69assumption in classical theory, property values decline 70with distance from the center of the city (Alonso, 1960). 71A common opinion of follow-up researches is that urban 727374===== PAGE 10 (score 14, numeric-paren 0, table-words 4, var-words 3) =====7576784 77Chinese Geographical Science 2015 Vol. 25 No. 6 78 79Table 4 General statistical descriptions of calculated distances 80Start of distance 81Distance 82Maximum 83Minimum 84Mean 85Standard error 86DGC 8714.68 882.19 897.26 900.79 91DSC_HK 9213.90 931.96 945.73 951.36 96DSC_WC 9711.69 981.98 995.44 1001.06 101Gravity points of lakes 102DSC_HY 1039.26 1040.38 1054.81 1061.76 107DGC 10815.81 1091.43 1107.53 1110.15 112DSC_HK 11312.73 1140.45 1154.42 1160.29 117DSC_WC 11814.89 1190.34 1205.58 1210.19 122Value points 123DSC_HY 12410.90 1250.25 1265.41 1270.31 128Notes: DGC is distance to geographical center; DSC_HK is distance to sub-center in Hankou District; DSC_WC is distance to sub-center in Wuchang District; 129DSC_HY is distance to sub-center in Hanyang District 130 131 132Fig. 8 Results of distance comparison for each value point 133 134of DSCi and DLSCi for each one. The results indicates 135that among all the 548 value points, 322 points (58.8%) 136are closer to the sub centers and 226 points (41.2%) are 137farther from the sub-centers compared to their closest 138urban lakes. 1393.4 Spatial characteristics in proximity to urban 140lakes 141The exponential function is a fair choice to describe the 142relationship between housing prices and the traveling 143time from CBD (Osland and Pryce, 2012). In this paper, 144we explore the spatial characteristics based on the esti-145mated results in housing price surface and various dis-146tances to sub-centers or lakes. The regression analysis 147was implemented both for the entire region (Fig. 9a) and 148for different sectors (Figs. 9b, 9c and 9d). The difference 149is that the distance in Fig. 9a is from the value point to 150its urban GC, where the distances in Figs. 9b, 9c and 9d 151are from the value point to its sub-center. The R2 in Figs. 1529b, 9c and 9d are higher than those in Fig. 9a, being es-153pecially significant in HK and HY (Fig. 9). The results 154indicate that, first, the prices in the three towns of the 155Wuhan City are to some extent independent from each 156other. With the increase of the distance to the sub-center, 157there is a clear downward trend for housing prices in 158HK and HY. Second, HK has the highest overall housing 159price level, followed by WC and then HY. Additionally, 160HK has the most significant decline in housing prices 161with the increase in the distance to the sub-center, while 162the decline is the slowest in WC. Furthermore, the R2 163are lower for the exponential function in WC. This 164might be due to some big lakes located in this town 165(e.g., L16) splitting up the built-up areas and resulting in 166sub-centers being not representative. 167To examine the relationship between housing pric-168es and distances to sub-centers at different scales, we 169use exponential models to fit the housing prices and 170the distance to sub-center of value points in 171ULBZ0–0.5km, ULBZ0.5–1km, and ULBZ1–2km (Fig. 10). 172The results show that, first, for HK′s urban lakes, the 173overall level of housing prices in ULBZ0–0.5km is 174higher than that in ULBZ0.5–1km. This becomes more 175obvious when getting closer to the sub-center. Sec-176ond, for HY′s urban lakes, the regression model of 177ULBZ0–0.5km is very close to that of the ULBZ0.5–1km. 178However, the overall level of housing prices in 179ULBZ0–0.5km and ULBZ0.5–1km is significantly higher 180than that in ULBZ1–2km. Third, for the urban lakes in 181WC, the overall housing price level in ULBZ0–0.5km is 182slightly higher than that in ULBZ0.5–1km, which is sig-183nificantly higher than that in ULBZ1–2km. 184185186===== PAGE 11 (score 4, numeric-paren 0, table-words 1, var-words 2) =====187188 189ZHANG Zuo et al. A GIS-based Spatial Analysis of Housing Price and Road Density in Proximity to Urban Lakes in Wuhan… 785 190 191 192Fig. 9 Relationship between housing prices and distances in different districts. Meanings of all abbreviations see Table 4 193 194Fig. 10 Relationship between housing prices and distance at different scales 195