STUDY ON SPATIAL AUTOCORRELATION OF URBAN LAND PRICE DISTRIBUTION IN CHANGZHOU CITY OF JIANGSU PROVINCE

This paper uses a spatial statistics method based on the calculation of spatial autocorrelation as a possible approach for modeling and quantifying the distribution of urban land price in Changzhou City, Jiangsu Province. GIS and spatial statistics provide a useful way for describing the distribution of urban land price both spatially and temporally, and have proved to be useful for understanding land price distribution pattern better. In this paper, we apply the statistical analysis method to 8379 urban land price samples collected from Changzhou Land Market, and it is turned out that the proposed approach can effectively identify the spatial clusters and local point patterns in dataset and forms a general method for conceptualizing the land price structure. The results show that land price structure in Changzhou City is very complex and that even where there is a high spatial autocorrelation, the land price is still relatively heterogeneous. Furthermore, lands for different uses have different degrees of spatial autocorrelation. Spatial autocorrelation of commercial lands is more intense than that of residential and industrial lands in regional central district. This means that treating land price as integration of homogeneous units can limit analysis of pattern, over-simplifying the structure of land price, but the methods, just as the autocorrelation approaches, are useful tools for quantifying the variables of land price.

Spatial Data Process Using Python Script and Visual Programming Language in ArcGIS

Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.

鄂西南亚热带常绿落叶阔叶混交林土壤空间异质性

大样地是监测森林生物多样性及生态因子动态变化的重要平台,以湖北木林子国家级自然保护区常绿落叶阔叶混交林为对象,依托15 hm2动态监测样地,对每个400 m2样方进行取样并测定7个化学性质指标,运用地统计与多元统计方法,分析了该大样地内不同土壤因子的富集程度、空间格局、变异性、相关性。结果如下:1)研究区土壤酸性较强,有机质、有效氮、全氮、有效钾丰富,有效磷、全磷亏缺;2)有效氮、全氮、有效磷呈东高西低,p H呈西高东低,有效钾呈斑块状分布,有机质呈中间高,西北与东南低的"凸"型分布,其中以有效钾空间结构最复杂(A=50 m,D=1.96),p H空间结构最简单(A=180 m,D=1.83);3)土壤因子的块基比范围为0.14—0.69,14对因子显著相关(P<0.05),其中以全磷的空间自相关最强,并与其余因子显著相关;4)土壤因子的变异系数范围为0.05—0.34,其中全磷、有效磷变异系数最高,分别为0.29和0.34,p H变异系数最低(0.05),土壤全磷在主成分中具有最大载荷(2.27)。结果表明:1)木林子常绿落叶阔叶混交林土壤酸性较强,除了磷素亏缺,其余土壤养分富集;2)土壤的变异性不强,空间分布具有明显的自相关特征,并具有一定的尺度效应;3)除了土壤有效磷以外的土壤因子之间存在较为普遍的相互联系;4)土壤全磷对于土壤的综合变异最具代表性。

Spatio-temporal association analysis of county potential in the Pearl River Delta during 1990-2009

According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county＇s potential in each year by means of expanded potential model. Based on that, the spa- tio-temporal association patterns and evolution of county potential were analyzed using spa- tio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows： （1） The global spatio-temporal association of county po- tential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties＇ potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association （i.e., HH, LL, HL and LH）. （2） The distribution difference and its change of local spatio-temporal association types of county potential were obvious. Spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribu- tion pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. （3） The local spa- tio-temporal association patterns of county potential among the three zones presented significant disparity, and obvious difference between the eastern and central zones tended to decrease, whereas that between the western zone and the central and eastern zones further expanded. （4） Spatio-temporal autocorrelation methods can efficiently mine the spatio-temporal asso- ciation patterns of county potential, and can better reveal the complicated spatio-temporal inter- action between counties than ESDA methods.

Impact of environmental factors on water quality at multiple spatial scales and its spatial variation in Huai River Basin, China

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators,namely, ammonia nitrogen(NH_4^+-N), permanganate index(COD_(Mn)), total phosphorus(TP) and total nitrogen(TN) at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors(land use pattern) and natural factors(river density, elevation and precipitation) to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with COD_(Mn), NH_4^+-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed "small-scale" effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.

Differentiation characteristics and influencing factors of ecological land rent among provinces in China

Ecological land rent is the excess profit produced by resource scarcity, and is also an important indicator for measuring the social and economic effects of resource scarcity. This paper, by calculating the respective ecological land rents of all the provinces in China for the years 2002 and 2007, and with the assistance of the software programs ArcGIS and GeoDA, analyzes the spatial differentiation characteristics of ecological land rent; then, the influencing factors of ecological land rent differentiation among the provinces are examined using the methods of traditional regression and spatial correlation analysis. The following results were obtained： First, ecological land rent per unit of output in China shows stable dis- tribution characteristics of being low in the southwestern and northeastern provinces, and high in Hebei and Henan provinces. There is also an increasing tendency in the central and western provinces, and a decreasing one in the eastern provinces. In general, the spatial distribution of ecological land rent per unit of output in China is quite scattered. Second, the total ecological land rent shows significant spatial aggregation characteristics, in particular the provinces in China possessing high total amounts of ecological land rent tend to be adjacent to one another, as do those with low total amounts, and the spatial difference characteristics of the eastern, central and western provinces are distinguished. The Bohai Rim, Yangtze River Delta and Pearl River Delta are shown to be highly clustering regions of total ecological land rent, while the western provinces have very low ecological land rent in terms of total amount. Third, population distribution, economic level and industrial structure were all im- portant influencing factors influencing ecological land rent differentiation among provinces in China. Furthermore, population density, urbanization level, economic density, per capita consumption level and GDP per capita were all shown to be positively related to total eco- logical land rent, which indicates that spatial clustering exists between ecological land rent and these factors. However, there was also a negative correlation between ecological land rent and agricultural output percentage, indicating that spatial scattering exists between ecological land rent and agricultural output percentage.