A Grid-surface Projection of Urban and Rural Population in China,

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1 1 A Grid-surface Projection of Urban and Rural Population in China, Tomoki NAKAYA * * Department of Geography, Ritsumeikan University, 56-1 Tojiin-kita-machi, Kita-ku, Kyoto , Japan Abstract: This study projects the geographical distributions of Chinese urban and rural population from 1990 to First, a new areal interpolation method is developed to convert the Chinese county dataset into a grid surface by using the DMSP/OLS Stable Night Light Image. Second, to predict county-scale populations, changes in the national and regional cohort sizes are estimated based on the national life table and regional cohort change rates between 1990 and Third, a procedure to estimate urban populations is developed to convert the ratio of agricultural to non-agricultural households into the urban - rural household ratio at a county level. Finally, the county populations are converted into a grid-surface (LUGEC 20km grid). As a result, population surfaces for urban and rural residents are obtained from 1990 to Keywords: areal interpolation, China, cohort change, population projection, DMSP/OLS 1. Introduction Population distribution is the most fundamental information needed to estimate a food consumption surface. As the first step in this process we estimate the distributions of urban and rural populations in 1990, because a big difference is seen between the food consumption patterns of urban and rural residents in China. Secondly we project the population distribution from 1995 to The spatial unit used for our population estimations is the LUGEC 20 km grid; the LUGEC (Land Use For Global Environmental Conservation) project defined a grid of 10' latitude x 15' longitude (about 20km x 20km). Such grid -based estimations can enable spatial accuracies to be unified over the whole study area. Such estimations are also convenient for combining the population data with other physical data relevant to food production, such as land-use and natural environmental data. However, in China, the spatial accuracies of the data that can be used are basically coarse and cannot be matched with each other. To overcome this problem, this study proposes a method of population projection composed of many steps combining various sources of information. Firstly, we use the nighttime light distribution acquired by remote sensing to develop an areal interpolation technique for converting county population data into grid-based data 1). Secondly, we detail a method of calculating the population projection at a county scale by using other, coarser scale information 2). Finally, we show a method to estimate the ratios of urban and rural populations for each county 3). With these methods, we can obtain the urban and rural household population for each 20 km grid cell from 1990 to An Method of Areally Interpolating Population The highest-resolution unit with reliable population statistics for China is the county. This study uses the 2420 (non-island) counties defined by the 1990 Chinese census. The areas of the counties vary dramatically (the average is 3876km 2 ): the inland counties are large but their population densities are quite low, while urban counties are quite small but with high population densities. The county statistics data set was made by Prof. G. W. Skinner (University of California-Davis) and can be downloaded as GIS data files from the website of SEDAC (the Socioeconomic Data and Applications Center) of CIESIN (the Consortium for International Earth Science Information Network).

2 2 When transforming the information from one type of areal data into another type of areal data, i.e. areal interpolation, some errors are unavoidable. However, the reliability of the transformation is improved if the estimation of the distribution to be transformed is more detailed than the 2 types of areal units. Such interpolation aided by indirect information is called intelligent interpolation. Martin 4) compared several methods of areal interpolation given the coordinates of representative points in population distribution for each areal unit. However, such information often cannot be obtained in less developed countries. An alternative method advocated by Fisher and Langford 5) is to utilize remote sensing images to distinguish between residential and non-residential areas. For the areal interpolation of population, this study employs images of nighttime light distribution over the earth s surface captured by DMSP/OLS (Defense Meteorological Satellite Program / Operational Linescan System), an American military satellite system. Since the existence of stable nighttime lights reflects the degree of human activity, the images are useful for estimating population distributions. Recently, DMSP/OLS images have been applied to many issues related to the distribution of human activ ities, such as identification of urban areas 6),7), assessing national GNPs and populations 8),9), and estimating populations of small areas in developed countries 10),11). In order to calculate the distribution of stable lights, we used a composite DMSP/OLS image made from a series of scanned data from 1994 to Each pixel contains a value of the percentage (0-100%) of light observed in scenes which were unobstructed by clouds; the values of 1% to 5% are replaced by 0% to exclude unstable lights 12) used 10% as the threshold value. The composite image has the same format as that of USGS GTOPO30, a global DEM dataset, in aspects of map projection and resolution (30 grid). In this study, we call this composite image containing the percentage values of light observation the Stable Light Image (SLI). Figure 1 shows the SLI around China. Figure 1 DMSP/OLS Stable Light Image around China ( ) A problem with using the SLI to estimate population distributions is that the values of SLI tend to be saturated at the upper limit, 100%, in urban areas. On the other hand, there are many inhabited non-urban areas where no nighttime light is observed. Note that areas where nighttime lights are observed do not necessarily correspond to inhabited areas, especially in less developed countries. In China, in areas where population densities are less than 1000 persons per square kilometer, the relationship between population and SLI values is extremely weak. Elvidge et al. 12) shows that compared with population, the lit areas in China are fewer than in other countries. The relation between the lit area and population differs according to the stage of industrial development. Therefore, in order to estimate the population distributions in the domain where nighttime lights

3 3 are not observed, we assume that the population distributions are related to accessibility to the nighttime lights. The index of the accessibility to lights is similar to that of population potential. We define this SLI potential as:. P = SLI exp( δ d ) i j j ij where P i is the potential value of the i th 30 grid cell; SLI j is the SLI value of the j th 30 grid cell; d ij is the distance between the i th and the j th grid cell; andis a parameter. We specify a model for population estimation as: D = β + β SLI + β P. β 2 β4 i 0 1 i 3 i where D i is the estimated population of the i th 30 grid cell; k s are parameters. Aggregating the estimates for the 30 grid cells according to each county, we calibrate the model to estimate the parameters by minimizing the squared differences between the observed and estimated log-transformed populations of each county. The coefficient of determinant is 0.7. With the exception of underestimations in the prediction for counties having over 10,000 persons per square kilometer, the model generally fits well. A previous report 1) provides more detail, including comparison with other models. Finally, we specify the equation to allocate county populations to the 20 km grid cells by using allocation weights from the population estimation based the 30 grid cells: EP = CP D k k i i j Sk D j where EP k i is the estimated population of the i th 30 grid cell belonging to the k th county; CP k is the census population of the k th county; Sk is the set of 30 grid cells belonging to county k. By allocating the population according to the weights, the total population for each county and the total of the transformed values in the 30 grid cells should be the same, like Tobler s smooth pycnophylactic interpolation 13). By using this calibration, the proposed method achieved greater reliability at the county level than does the method of Deichmann 14) who utilizes accessibility to urban areas. With regard to underestimation in counties with high population densities, most of those counties are smaller than the 20 km grid, so we can avoid the transformation errors in most cases. Figure 2 shows the estimated Chinese population surface in In the LUGEC project, we use the same allocation method for making grid datasets of other socio-economic indicators, because the geographical distributions of these basically correspond to that of population. Figure 2 Estimated Chinese population surface in 1990

4 4 3. Regional Population Projection 3.1 Data This study proposes a method of population projection combining cohort changes on three spatial scales: national, city-region, and county. Although relatively reliable statistics for each county were provided with the 1990 Chinese census, we could not obtain other population statistics for age compositions in different years; as a result we cannot calculate cohort changes at a county level. Instead of using census statistics, we obtained a Japanese book containing many regional statistics, Chugoku Furyoku 99 edition (Soken), which was compiled under the supervision of the Chinese Statistical Bureau. The smallest spatial unit in the book is city-region defined by aggregating several counties. To be consistent with the counties in the 1990 census, we identified 364 city-region units. The city-region statistics have a table representing age composition at 5-year intervals, but not sex composition. Because the sources are not provided in the book, the reliability is not confirmed. We compared the provincial populations presented in Furyoku and the 1% sampled population survey in 1995 (Almanac of China's population 1996), and identified a small percentage gap between the two. Figure 3 shows the spatial units used in this study: city-region and county. 3.2 Assumptions This study simply utilizes a life table made by Oobayashi 15) in the 1980s for the survival rates of the 0-74 age groups and cohort changes between the 1990 census and the 1995 Furyoku for the survival rates of the over-75 age groups. Survival rates improved mainly in the older age groups. We expect that using the cohort changes between 1990 and 1995 accurately capture the recent improvement in the survival rates in the older age groups. However, there are some unnatural cohort changes between 1990 and 1995 in the younger age groups, probably because of problems in the Chinese census method. In order to avoid such unnatural changes, we assigned the survival rates in the 1980s to the younger generations. See the previous report for details 2). Moreover, the changes in the city-region cohort are highly affected by temporal phenomena. To exclude extreme trends in cohort changes, for each 5-year age interval we replaced the values above the upper 5th percentile with the 95th percentile value and the values below the lower 5th percentile with the 5th percentile value. For the population projection at the county scale, we combine 3 cohort changes: (1) national cohort changes, (2) city-region cohort changes and (3) county cohort changes. The fundamental ideas are summarized as: Figure 3 Areal units (city-region and county) (Step 1) We model the national cohort change using the above-mentioned method integrating the life table

5 5 and the observed cohort change. (Step 2) Regarding regional cohort changes, we apply the observed city-region cohort changes between 1990 and 1995 to the future projection of county populations. We assume that every county in a city-region unit has the same cohort change rates; it should be noted that the population structures for the counties in a city-region are different from each other. (Step 3) The population changes aggregated from county cohort changes should be the same as the national population change based on the national cohort changes in step 1. Correction of county cohort changes is necessary. Other assumptions are. 1. The base line of the projection is the state in The population size and age-composition of the Chinese army will be the same as the value in the 1990 census. 3. International population movements are ignored. 4. Mortality structure will not change after The national total fertility ratio will be 2.0 until 1995, after which it will become 1.8 according to an article in the Almanac of China s population For each city-region unit, the sex ratio for each age group is the same. 3.3 The cohort projection of the total Chinese population The national population change is modeled: p = cp k+ 1, t+ 1 k kt, p = c f sp 0 4, t kt, k kt, k= p = c p 90 +, t kt, k= for k = 0-4,, where pkt, is the population of age interval k at time t; c k is the survival rate of the population after 5 years (1- c0 4almost corresponds with the infant death rate); f kt, is the fertility rate of women in age interval k at time t; s k is the ratio of women in the population of age interval k. The projection until 2050 resulting from this equation is shown in Figure 4. Figure 4 National population projection of China,

6 6 China will experience the highest population ( billion) during ; after that time, the population will decline. The size of the reproductive population, in particular, that of the age groups, will fluctuate due to the effects of past baby booms, and then decline after Instead of the decline seen in the reproductive population, the proportion of elderly people will rapidly increase; the proportion of the population in over-65 age groups will be 11.8% in 2025 and 18% in 2050 in comparison with 5.5% in It is reasonable to estimate that the Chinese population will be far lower than the old common estimates of 1.6 billion. The projection is based on the assumption that the national government maintains its policy of allowing only one child for each couple, by which the national total fertility ratio becomes 1.8. Since rural residents, in particular, ethnic minorities, tend to have deep-rooted objections toward the policy, the abolition or relaxation of the policy in the future 13) might affect the long-term projection. 3.4 Cohort projection at the county level v be the rate of cohort change in city-region i, age interval k between 1990 and The rate of cohort Let ik, rc change in county i, age interval k at time t,, should satisfy the equation of national balance of population change: where i rc rp = c p ap + kt, kt, k 1 rp is the population of county i, age interval k at time t; and ap k + 1 is the population of the national army of age interval k+1. Assuming that rc should be proportional to v ik,, we define rc c p ap, = ( kt, kt, k+ 1) ik v v rp i ik, rc : The population projection at a county level is performed the same way as the national projection. The equation of population change of county j belonging to city-region i is modeled as below: cp = rc cp jk, + 1, t+ 1 jkt,, cp = rc rf rs cp j,0 4, t+ 1 i,0 4, t ik, jkt,, k = cp = rc cp where jkt,, j,90 +, t+ 1 i,90 + jkt,, k= cp is the population of county j, age interval k at time t; rf and rs ik, are, respectively, the age-specific fertility rate and ratio of women of age interval k in city-region i. Regarding the age-specific fertility rate, we first calculate the ratio of the actual number of births to the expected number of births following the national average values of the age-specific fertility rate during Then multiplying the ratio with the national average values of the age-specific fertility rate at time t, we get the estimate of rf for each city-region. 4. Projection of Urban and Rural Populations Since the Chinese definitions of urban and rural population have been changed often, it is difficult to obtain a consistent time series of the population composition in China 16). According to the definition of the 1990 census, the national ratio of urban households is over 50%, a number which is clearly overestimated. In order to estimate more realistic urban and rural populations, the statistics of agricultural and non-agricultural households

7 7 are useful. As the non-agricultural population is almost matched to the urban population, we can use time series data of these populations. A report from the 1990 census 17) has a table showing a substantial urban population for each province. This study uses the relationship between the substantial urban and non-agricultural household populations to estimate the urban-rural household population at a county level. y = x R 2 = Figure 5 Correlation between urban and non-agricultural population at the province level Figure 5 shows a clear correlation between provincial non-agricultural household populations and substantial urban populations estimated by the Chinese census report. Based on the regression line with the constant term fixed as zero, there are times as many members of urban households as there are me mbers of non-agricultural households. Let h i be a correction factor defined as the substantial urban household population divided by the estimated urban household population for each province i. The urban household cp population of county m, mu, cpmu, = 1.178cngmhi, is estimated as below: where i is the index of the province which the county m belongs to; cng m is the non-agricultural household population of county m. Then letting tcp m be the total population of county m, the agricultural household population of the county, cp mr,, is estimated as below: cp = tcp cp mr, m mu, According to this model, if the non-agricultural population ratio is more than about 0.85, then the estimate of the urban population ratio generally exceeds 1.0. Since the results of several kinds of curvilinear regressions are worse than those of the linear regression, we apply simple rules based on the result of the linear regression: (Rule 1) If the estimated ratio of the urban household population in a county exceeds 1.0, then the ratio is 1.0. (Rule 2) After applying rule 1 to all counties in a province, we aggregate the county estimates of urban household population into a provincial value. If the estimated urban household population of the province is less than the real one, we allocate the difference to each non-saturated county (where urban population ratio < 1.0) in proportion to the non-agricultural population ratio of the county. (Rule 3) In cases where differences remain between the real and estimated values after rule 2 was applied, we iterate the allocation of urban household population according to rule 2 until the difference disappears.

8 Figure 6 The transition of agricultural household population rate for each region in China 8 In order to project the future urban household population, we use the trend curves of provincial agricultural household populations between 1982 and 1990: pp i, y κi = 1 + λ( y 1982) i where pp i,y is the ratio of agricultural household population of province i, year y;? and? are parameters. The average of the coefficient of determinant of these provincial trend curves is Figure 6 shows the trends of aggregated estimates of agricultural household population ratios for each of the 7 districts in China until We can get the provincial estimates of the urban household population based on this trend model and then project the urban household population for each county until In the projection, we again apply rules 1-3 to allocate urban household populations to each county on the basis of the 1990 county non-agricultural household population. 5. Conclusion This study projects the populations of urban and rural households in each county until 2050 by combining 3 different population statistics. We converted these estimated populations into the LUGEC 20 km grid system by using the areal interpolation method explained in section 2. We postulated that the population distribution for each county could be estimated by the nighttime light distribution around The postulation was shown to be valid for predicting the overall population distribution in China; the best merit of this interpolation method is its ability to produce a realistic distribution in large counties with quite small population density. Figure 7 shows the estimated population surfaces in 1990 and 2025 when the Chinese population is expected to peak. These maps indicate that there are a few areas where the urban household population decreases, and scattered around the large urban areas, there are areas where the total population decreases. It shows that the recent trend of urban growth in China is attained by migrations of agricultural household populations from the surrounding rural areas where their total populations are decreasing. In the more rural areas remote from the large cities, rural household populations are increasing. It should be noted that the population projection in this study is based on the cohort changes between 1990 and 1995, and that we ignore the long-term feedback mechanisms between economic activity and population movements. The trends will be affected by policy changes and other feedback from the present population movements, such as remittance and return migration that introduce regional transfers of income from urban to rural areas 16). The movement of labors from rural to urban areas has accelerated since the economic reforms of Such rural laborers in urban areas might move back to rural areas. If so, we should consider these effects

9 9 and incorporate them into new economic and population models. Migration is also affected by the transition of industrial structure in Chinese macroeconomics 18). Therefore, there is some uncertainty about how long the recent trends in population redistribution will continue. Considering the difficulties that simple trend models have in taking into account the transitions of many conditions, we should compare several possible scenarios of economic transition. When the 2000 census becomes available, we can identify any new trends in population redistribution at a county level and we can construct a more reliable predictive model of the Chinese population. Population ,000 5,000-50,000 50, , , ,000 More than 200,000 Urban Popul ation ,000 5,000-50,000 50, , , ,000 More than 200,000 Population ,000 5,000-50,000 50, , , ,000 More than 200,000 Urban Popul ation ,000 5,000-50,000 50, , , ,000 More than 200,000 Population Change Rat e < 50% 0-50% 0-50% % % > 250% Urban Popul ation Change Rat e < 50% 0-50% 0-50% % % > 250% Figure 7 The Chinese total and urban household population surfaces LUGEC 20km grid

10 10 References 1) Nakaya, T. (1999): Population surface estimation and grid interpolation in China using DMSP/OLS stable night light image. In Otsubo, K. (ed.) LUGEC Project Report V, Centre for Global Environmental Research, Tsukuba, (In Japanese with English abstract) 2) Nakaya, T. (2000): Projection of geographical distribution of Chinese population up to In Otsubo, K. (ed.) LUGEC Project Report VI, Centre for Global Environmental Research, Tsukuba, (In Japanese with English abstract) 3) Nakaya, T. and Shimizu, Y. (2000): Geographical distributions of consumption for three major grains in China. In Otsubo, K. (ed.) LUGEC Project Report VI, Centre for Global Environmental Research, Tsukuba, (In Japanese with English abstract) 4) Martin, D. (1996): An assessment of surface and zonal models of population, International Journal of Geographical Information Systems, 10, ) Fisher, P.F. and Langford, M. (1996): Modeling sensitivity to accuracy in classified imagery: a study of areal interpolation by dasymetric mapping, Professional Geographer, 48, ) Imhoff, M.L. Lawrence, W.T., Stutzer, D.C., and Elvidge, C.D. (1997): A technique for using composite DMSP/OLS city lights satellite data to map urban area, Remote Sensing and Environment, 61, ) Imhoff, M.L., Lawrence, W.T., Elvidge, C.D., Paul, T., Levine, E., Privalsky, M.V., and Brown, V. (1997): Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United Sates, Remote Sensing and Environment, 59, ) Elvidge, C.D., Baugh, K, Kihn, E.A., Kroehl, H.W., Davis, E., and Davis, C. (1997): Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption, International Journal of Remote Sensing, 18, ) Elvidge, C.D., Baugh, K, Hobson, V.R., Kihn, E.A., Kroehl, H.W., Davis, E.R., and Cocero, D. (1997): Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest, Global Change Biology, 3, ) Sutton, P., Roberts, D., Elvidge, C. and Meij, H. (1997): A comparison of nighttime satellite imagery and population density for the continental United States. Photogrammetric Engineering and Remote Sensing, 63, ) Nakayama, M. (1997): Developing population database with DMSP/OLS imagery, Proceedings of International Conference on Modeling Geographical and Environmental Systems with Geographical Information Systems, 2, ) Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W., and Davis, E.R. (1997): Mapping city lights with nighttime data from the DMSP operational linescan system, Photogrammetric Engineering and Remote Sensing, 63, ) Tobler, W. (1979): Smooth pycnophylactic interpolation for geographical regions, Journal of the American Statistical Association, 74, ) Deichmann, U. (1996): Asia Population Database Documentation, Part II Raster Data, paper prepared for USGS UNEP GRID Web site part2.html. 15) Oobayashi, S. (1992): An evaluation of static population statistics in China, In Hayase, Y. (ed.) Population dynamics in China, Shinyosha. (In Japanese) 16) Wakabayashi, K. (1996): Gendai Chugoku no Jinko Mondai to Shakai Hendo (Population Problems and Social Change in Contemporary China) Shinyo-sha. (In Japanese) 17) Sa, Z., So, K., and Kaku, S. (1996): Data analyses of the forth Chinese national census. China Higher Education Press. (In Chinese) 18) Ma, Z. (1999): Temporary migration and regional development in China. Environment and Planning A, 31,