Regional density functions and growth patterns in major plains of China,

Transcription

1 Papers Reg. Sci. 80, (2001) c RSAI 2001 Regional density functions and growth patterns in major plains of China, Fahui Wang Department of Geography, Northern Illinois University, DeKalb, IL , USA ( wang@geog.niu.edu) Received: 8 March, 2000 / Accepted: 5 September 2000 Abstract. This research analyzes the spatial patterns of regional development in China through the change of population density functions from 1982 to 1990, using several online digital data sets. To minimize the influence of physical environment on population densities, the study areas are limited to four major plains of China: the Northeast (Dongbei), North China (Huabei) and Hubei- Hunan (Lianghu) Plains, and the Sichuan Basin. A gravity-based delineation approach is used to delineate the influence regions of 17 cities. Regional densities in China decline with distance from a city, similar to western countries. In addition, areas close to central cities grew faster than remote areas, described as a trend of centralization. However, regions with strong core growth are generally associated with stagnant hinterlands ( core growth-hinterland stagnation ); and regions with moderate core growth are usually matched by similar growth rates in the hinterlands ( spread through growth ). JEL classification: O18, O53, R11, R58 Key words: Regional density functions, regional growth patterns, China 1 Introduction Following the tradition of Von Thünen (1966), researchers (Beckmann 1971; Webber 1973; Wang and Guldmann, 1997) have developed economic models to explain the regional density pattern theoretically. Rural residents farther away from a city pay higher transportation costs for shipping agricultural products to the urban market and for gaining access to industrial goods and urban services, I would like to thank Jean-Claude Thill and three anonymous referees for their helpful comments and Lenny Walther for his cartographic assistance. An earlier version of this note was presented at GeoInformatics 2000 (The Association of Chinese Professionals in Geographic Information Systems Annual Conference), June 21-23, 2000, Monterey Bay, California.

2 232 F. Wang and are compensated by cheaper, and hence, more land. The resulting regional population density declines with distance from a city, characterized by various density functions (Parr 1985; Parr et al. 1988; Parr and O Neill 1989). The theoretical models are built on assumptions of a free market economy. Is such a density pattern, commonly observed in western countries, also applicable to a socialist country such as China? This is the first question addressed in this research note. According to Gaile (1980), economic development in the core (city) impacts the surrounding (suburban and rural) region through a complex set of dynamic spatial processes (intraregional flows of capital, goods and services, information and technology, and residents). If the processes result in an increase in activity (e.g., population) in the periphery, the impact is spread. If the activity in the periphery declines while the core expands, the effect is backwash. Such concepts help us understand core-hinterland interdependencies and various relationships between them. One common approach is to use population density functions to characterize regional structure and assess the changes over time. For example, Barkley et al. (1996) used the cubic spline function to examine the structural change of density patterns in eight functional economic areas in the southeast of the USA from 1980 to Applying a similar approach to China can be particularly interesting because of the changes brought about by its economic reform and open-door policies. How have the cities influenced the development of rural areas? How is the development pattern in one region different from another? The purpose of this research is two-fold. First, to analyze the regional density functions in China, and second, to examine its growth patterns during Processing data and defining the study area It is generally known that data are less plentiful and less reliable in developing than in developed countries, (Mills and Tan 1980, p. 313). Because of lack of reliable data, studies on China s population density patterns at a fine scale are scarce. Most studies are traditional descriptive analyses with no reference to density functions (e.g., Zhao 1994, p ; Meng and Jia 1993). Some online digital data sets of China, particularly GIS data, create many new research opportunities. Most of the data for this research are downloaded from the web site of the Socioeconomic Data and Applications Center (SEDAC) of the Center for International Earth Science Information Network (CIESIN 2000). Specifically this research uses two of the SEDAC data sets: the China Administrative Regions GIS Data: 1:1M County Level, 1990 and the China County-Level Data on Population (Census) and Agriculture, Keyed to 1:1M GIS Map. The digital map of China s county boundaries improves the accuracy of density measurement, and creates opportunities for advanced spatial analyses. In addition, Shuming Bao at the China Data Center of University of Michigan provided the 1982 Population Census data.

3 Regional density and growth of China, a 5b N Study Areas Areas >= 25% Cultivation Kilometers Fig. 1. Defining the study areas Matching the 1982 population census data with the GIS coverage and with the 1990 census data has been a major challenge of this research. Most of the unmatchable codes were corrected by comparing two atlases of China published in two different years (Cartographic Press of China 1983; Xing-qiu Cartographic Press 1998). Another effort was made to match the records by merging the county units in 1982 or 1990 whenever multiple county units in one year correspond to one county unit in another year. Obviously, the variation of population density reflects the influence of physical environment such as topography, weather and land use suitability. The eastern monsoon China with half of China s territory has over 90 percent of the country s population, and the northwest arid China and Tibetan frigid plateau are sparse in population (Zhao 1994, p. 131). As the interest of this research focuses on the impact of cities on their rural hinterlands, one wishes to identify areas with similar natural environment like the uniform plains in Von Thünen s model. Limited by the availability of data, this is approached in a rather approximate way. From the China county-level data on agriculture, areas above a 25% cultivation ratio are identified; see Fig. 1. This ratio is approximately the average land cultivation ratio in China (24.3% at the county level). The areas on the map

4 234 F. Wang coincide well with China s plain areas including: (1) the massive North China (Huabei) Plain extending eastwards to the Shandong Peninsula, southeast to the Chang Jiang Delta and the Hangzhou Estuary, and westwards to the Weihe Plain; (2) the Northeast (Dongbei) Plain; (3) the Sichuan Basin; (4) the Hubei-Hunan (Lianghu) Plain; and (5) other minor plains such as the Sanjiang Plain (5a in Fig. 1) at the northeast corner, the Hetao Plain northwest of Beijing (5b in Fig. 1) and other small areas. The study areas are limited to regions (1) through (4) within which major cities are located. The selection of major cities will be discussed in the following section. The study areas cover a land area of million km 2, i.e., 11.1% of China s total, but have 47% of China s total population in 1982 or in The 766 county units in the study areas have an average population density of persons per km 2 in 1982 and p/km 2 in 1990, i.e., an 18% increase. 3 Delineation of influence regions of cities The analysis of regional density patterns starts with identifying central cities and their influence regions. This research builds upon the results from prior research by Gu (1991). Based on 33 variables such as total non-agriculture population, urban built-up area, urban population density, gross domestic product, transportation and telecommunication volumes, retailing values and others, Gu develops a comprehensive strength index for all cities in China. Using this index, Gu identifies 31 central cities and divides China s urban system into 31 urban economic regions (UER). Gu recognizes that the 31 UERs could be aggregated to form a higher level of nine mega-uers, led by nine large cities. However, the 31 UERs are more coherent than the 9 mega-uers in terms of intensity of intraregional interactions and are more suitable for this analysis. Among the 31 central cities, 17 fall within the study areas (see Fig. 2). These 17 cities are not necessarily the largest 17 cities in the study areas, but each city is indeed the largest city in its own region. For example, Tianjin, one of the largest cities in China, is very close to Beijing, but only the larger city Beijing is chosen as the central city of the region. Gu s study involves only cities and their linkages to each other. His delineation of urban economic regions is conceptual with only illustrative boundaries. The first task of this research is the delineation of influence regions or hinterlands of the 17 central cities. Without any information of economic connection between cities and counties, such as transportation volumes, telecommunication flows or financial transactions, we have to rely on a simple approach, which is based on the well-known gravity model. For instance, a county j is influenced by a city i with an impact measured by: I i = C i /r β ij, where C i is the population size of city i, r ij is the distance between county j and city i, and β is the distance friction coefficient. A county is assigned to the

5 Regional density and growth of China, Kilometers Harbin Changchun N Shenyang Shijiazhuang Beijing Xi an Zhengzhou Jinan Qingdao Xuzhou Chengdu Wuhan Hefei Nanjing Shanghai Chongqing Changsha Fig. 2. Region of 17 cities by the gravity-model approach influence region of a city if the impact of this city is the largest among those of all cities. A similar method can be found in Berry and Lamb (1974) among others. This research uses β =2.1, a value obtained by Yang (1990) in his study of gravity models for analyzing the interregional passenger flow patterns in China. 1 Figure 2 shows the delineation of regions, based on the 1982 population of the cities (C i ). It is assumed that the regional boundaries remain the same in 1990 for comparison of density functions in the same region over time. 4 Regional density functions and growth patterns A regional density function describes how population density D changes with distance r from a central city regardless of direction. Following Alperovich (1982), four simple functions are tested: (A) linear, D r = a +br; (B) log-transformation of exponential, ln(d r )=a + br; (C) reverse-exponential (linear between density and 1 A different β yields different hinterlands. A smaller β makes distance matter less, and therefore expands the hinterland of a larger city and shrinks the hinterland of a smaller city. However, by experimenting with various β ranging , density functions remain relatively stable. Regional boundary changes often occur in remote rural counties with similar low densities, and do not affect the regional density functions much.

6 236 F. Wang logarithm of distance), (D r )=a + bln(r); and (D) log-linear, ln(d r )=a + bln(r). Function B indicates that the density decreases by a constant percentage (slope b) as the distance increases by one unit. Function C implies that the density decreases by a constant interval (slope b) as the distance increases by one percent. In a graph, Function C generally shows a faster drop initially and then a flatter curve in remote areas than Function B. Unlike urban density patterns often characterized by a negative exponential function B (McDonald 1989), the reverse-exponential function C has the best fitting power for the regional density patterns in the study areas. Among the four density functions, the reverse-exponential function yields the best R 2 for the 17 regions. Regression results are presented in Table 1. The rest of this section is devoted to a discussion of these results. Table 1. Regression results for the 17 regions in 1982 and 1990, D r = a + bln(r) Central city No Obs. a b R 2 a b R 2 a/a b/b Jinan Wuhan Qingdao Chongqing Chengdu Harbin Shenyang Shanghai Shijiazhuang Beijing Zhengzhou Xuzhou Changchun Nanjing Changsha Xi an Hefei Notes: 1 significant at 0.01; 2 significant at 0.1; 3 not significant; otherwise, significant at First, overall the function fits the regional density patterns well in most regions with an average R 2 =0.49 in 1982 and 0.47 in 1990, except in the Wuhan region. 2 Declining density with distance from a central city indicates the diminishing influence of the city. This is consistent with predictions of theoretical models (Beckmann 1971, among others). However, those models are typically built on assumptions only valid in a market economy. Why do regions in China, 2 The Wuhan region is exceptional because of incomparable administrative boundaries of different cities in the region. For example, in 1990 the city of Wuhan had a density of 3012 person per km 2 in an area of 1341 km 2 including a significant portion of rural areas like most cities. However, for some reason, small cities such as Fuyang and Xiangfan in the region have narrowly-defined municipality areas of only 60 km 2 and 113 km 2 respectively, which are almost entirely urbanized areas. Consequently the data show that they have even higher densities than Wuhan. Fitting the pattern by any of the four functions is not appropriate. This problem is less noticeable in other regions.

7 Regional density and growth of China, under the socialist regime, conform to such regularity? Wang and Zhou (1999, p. 279) argue that people in China behave in response to opportunities available to them in very much the same way as those in a market economy, though their opportunities may be limited. Secondly, both the intercept a and gradient b (absolute value) in Table 1 have increased from 1982 to 1990 in all regions. A higher intercept a and a steeper gradient b over time imply a faster growth rate in areas closer to the central city (see Fig. 3 for instance). In other words, a county close to a central city not only attracts and supports a higher population density, but also experiences faster growth. Based on the 1982 and 1990 census data, such faster growth near a city is not attributed to a higher natural growth rate in these areas. In fact, remote rural areas generally have higher natural growth rates than areas close to cities. 3 The faster growth there is more likely caused by population migration from other areas. This trend of regional centralization 4 is commonly observed in developing countries such as China, a phenomenon described by McGee (1991) as kotadesasi or rural urbanization. The kotadesasi areas are relatively important well-developed economic regions, which may have several large core cities, a comparatively high-density of urban settlements, and transportation networks providing good accessibility (Zhou 1991, p. 89). The third observation concerns the different growth patterns among regions, evidenced by the changes of parameters a and b from 1982 and 1990 at various scales. Let a/a be the change rate of intercept a (i.e., a/a = (a 1990 a 1982 /a 1982 ), and similarly b/b be the change rate of intercept b (absolute value). By examining the last two columns in Table 1, we can easily see a positive correlation between a/a and b/b. That is to say, if a region has a faster growing central city (i.e., a larger a/a), the regional density in 1990 declines with an even steeper gradient than in 1982 (i.e., a larger b/b). To further illustrate this finding, two regions with contrasting trends are selected. Figure 3 shows the Xi an region with the greatest increases of a and b. Figure 4 shows the Jinan region with the least changes of a and b. In order to highlight the changing patterns, distance from central city (x-axis) in both figures is on a logarithm scale. In Fig. 3, a strong growth in the central city Xi an is associated with a greater disparity of growth rates across its hinterland, where areas near Xi an gain significant growth and remote areas are stagnant. In the terms of Barkley et al. (1996), it belongs to the pattern of core growth-hinterland stagnation. In Fig. 4, moderate growth in the central city Jinan is accompanied 3 A simple regression between natural growth rates (g) and distances from central cities (d) in the study area yields: g = d(n = 766). The coefficient of d is positive with a t-value of 5.58, statistically significant at the level, indicating higher natural growth rates in more remote areas. 4 In contrast to the centralization at a regional scale is decentralization within urban areas. Studies on urban density patterns of China such as in Beijing (Wang and Zhou 1999) and Shenyang (Zhou and Meng 1997) show that inner cities have lost population to suburbia in recent years, a process called decentralization or suburbanization. The change of intraurban structure could not be identified from our data aggregated at the county level, which often aggregate an inner city and its suburbs as one unit.

8 238 F. Wang 2500 Density (p/sq_km) Distance from central city (km) Fig. 3. Growth pattern in Xi an region 2500 Density (p/sq_km) Distance from central city (km) Fig. 4. Growth patern in Jinan region by a similar growth rate across its hinterland, though urban-nearby areas still grow slightly faster than remote areas. It bears much resemblance to the pattern of spread through growth in Barkley et al. s terms. Regions of Hefei and Changsha share a similar growth pattern with the Xi an region. During , these cities have experienced significant growth, and much faster than their hinterlands. Hefei and Changsha started from their relative medium sizes, and their rapid growth benefited from being the capital cities of Anhui and Hunan provinces respectively. Xi an s growth can be attributed to its military industries, many of which went through privatization during the 1980s. On the other end, regions of Qingdao, Chongqing and Shenyang share a similar growth pattern with the Jinan region with less tendency towards centralization. As the old industrial bases in their respective regions, these cities grew moderately during the 1980s. Such relatively slow growths in these cities were almost matched by their hinterlands. Part of the reason for less centralization was the more active small town development in these regions. 5 The growth patterns in other regions are between the two cases. 5 In the case of Qingdao, R 2 in Table 2 drops from in 1982 to in It may be attributed to the fast growth of other medium and small cities in the region, which weakens the role of the central city.

9 Regional density and growth of China, Conclusion This study analyzes the regional density functions and the spatial patterns of regional growth in China from 1982 to To minimize the impact of natural environment on population densities, the study area is confined to four major plains of China: Northeast (Dongbei) Plain, North China (Huabei) Plain, Hubei- Hunan (Lianghu) Plain, and Sichuan Basin. Based on the gravity model, the study area is divided into 17 regions surrounding their respective central cities. Findings are summarized in the following. First, rural land use more distant from a city is less intensive and supports a lower population density. Such pattern is predicted by economic models with assumptions that are only valid in a market economy. Why do regional densities in China also conform to the same pattern? It makes us wonder if many economic forces are universal and inescapable in a socialist country (see also Wang and Zhou 1999, p. 279). Secondly, regional densities in China decline rapidly from urban to rural land use and level off towards remote rural areas. Unlike urban density patterns often characterized by a negative-exponential function, such a pattern is better fitted by a linear function between densities and logarithm of distances from a central city. Finally, in all regions from 1982 to 1990, core cities and nearby areas grow faster than distant rural areas, a process described as centralization. However, fast growing central cities tend to exert a backwash impact on their hinterlands, and their rural hinterlands are often stagnant. On the other hand, central cities with moderate growth are in the process of spreading growth, and are accompanied by a similar growth rate in their hinterlands. Regions with less tendency towards centralization are usually backed up by more active small town development ( urbanization from below ). Future work can extend this research in at least two ways. If data of income or gross product per capita are available, we could analyze the regional spatial structure in terms of income distribution and productivity variation. Secondly, data at a smaller geographic unit level would help us identify further details of regional density variations and growth patterns, especially in areas within and near metropolitan areas. References Alperovich G (1982) Density gradient and the identification of CBD. Urban Studies 19: Barkley DL, Henry MS, Bao A (1996) Identifying spread versus backwash effects in regional economic areas: a density functions approach. Land Economics 72: Beckmann MJ (1971) Von Thünen revisited: a neoclassical land use model. Swedish Journal of Economics 74: 1 7 Berry B, Lamb R (1974) The delineation of urban spheres of influence: evaluation of an interaction model. Regional Studies 8: Cartographic Press of China (1983) Atlas of China (Zhong-guo Di-tu-ce, in Chinese), Beijing CIESIN (Center for International Earth Science Information Network) (2000) Web site at

10 240 F. Wang Gaile GL (1980) The spread-backwash concept. Regional Studies 14: Gu C-L (1991) A preliminary study on the division of Urban Economic Regions in China. Acta Geographica Sinica (Di-li Xue-bao, in Chinese) 46: McDonald JF (1989) Econometric studies of urban population density: a survey. Journal of Urban Economics 26: McGee, TG (1991) The emergence of Desakota regions in Asia: Expanding a hypothesis. In: Ginsburg N, Keppel B, McGee Tg (eds), The extended metropolis. University of Hawaii Press, Honolulu Meng X-J, Jia S-F (1993) The quantitative analysis of factors influencing population distribution in China. Geographical Research (Di-li Yan-jiu, in Chinese) 12: Mills ES, Tan JP (1980) A comparison of urban population density functions in developed and developing countries. Urban Studies 17: Parr JB (1985) A population density approach to regional spatial structure. Urban Studies 22: Parr JB, O Neill GJ, Nairn AGM (1988) Metropolitan density functions: a further exploration. Regional Science and Urban Economics 18: Parr JB, O Neill GJ (1989) Aspects of the lognormal function in the analysis of regional population distribution. Environment and Planning A 21: Von Thünen, JH (1966) Von Thünen s isolated state, translated by C. M. Wartenberg (ed Hall P), Pergamon Press, Oxford Wang F, Guldmann J-M (1997) A spatial equilibrium model for region size, urbanization ratio, and rural structure. Environment and Planning A 29: Wang F, Zhou Y-X (1999) Modelling urban population densities in Beijing : Suburbanisation and its causes. Urban Studies 36: Webber, MJ (1973) Equilibrium of location in an isolated state, Environment and Planning A 5: Xing-qiu Cartographic Press (1998) The Traffic Atlas of China (Zhong-guo Jiao-tong Tu-ce, in Chinese), Beijing Yang Q (1990) A model for interregional trip distribution in China. Acta Geographica Sinica (Di-li Xue-bao, in Chinese) 45: Zhao S-Q (1994) Geography of China: Environment, resources, population and development. John Wiley, New York Zhou Y-X (1991) The metropolitan interlocking region in China: A preliminary hypothesis. In: Ginsburg N, Keppel B, McGee TG (eds), The extended metropolis. University of Hawaii Press, Honolulu, pp Zhou Y-X, Meng Y-C (1997) Shenyang s suburbanization: comparison between China and the western countries. Acta Geographica Sinica (Di-li Xue-bao, in Chinese) 52: