Land use generalization indices are critical for producing normative multi-scale land use databases and maps,but there is still not a national criterion for these indices in China.Previous studies on land use data generalization indices often neglect macroindices such as area proportion of land use types and map load,and also ignore landscape effects on land use generalization indices.Here we constructed an index system of land use data generalization from macro-and micro-perspectives,and examined scale and landscape effects on land use data generalization indices based on the countrywide multi-scale land use data samples.We formulated relationships between land use data generalization indices and both scale and land use pattern metrics.We defined thresholds of land use data generalization indices on scales of 1:50000,1:100000,1:250000,and 1:500000.We verified our methodology and index thresholds through generalizing a county land use database.
LIU YaoLin1,2,JIAO LiMin1,2 & LIU YanFang1,2 1 School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China
This study focuses on spatial autocorrelation and the spatial distribution of urban land prices from a regional perspective.Taking Hubei province,China,as a case study area,spatial autocorrelation degree,spatial autocorrelation pattern,and the mechanism of its formation were discussed.The study employs Moran’s I,local Moran’s I,and Moran’s I correlogram to analyze spatial autocorrelation degree and its change along with contiguity order.Some local clustering hot spots are found.This paper uses semi-variance statistic for land price based on route distance to find the spatial autocorrelation scale.We also adopt spatial clustering based on a kind of composite distance to probe into the clustering characteristic of land prices.By Moran’s I and Moran’s I correlogram,we find that datum price of the cities in Hubei province has faint spatial autocorrelation degree at the first and the second-order contiguity.Spatial variance hints that the scale of the autocorrelation is about 200 km in route distance.Spatial clustering result indicates that the spatial distribution of city land price is a kind of hierarchy structure similar to administrative regions.From principal factors analysis and stepwise linear regression,we find that the value added of city secondary and tertiary industry and the urban population are two of the most influential factors to urban datum land price.The value added of city secondary and tertiary industry has higher spatial autocorrelation than urban datum land price and has a bigger autocorrelation scale.But urban population has little spatial autocorrelation.It can be inferred that the spatial autocorrelation of urban land price is mainly caused by economic spatial autocorrelation.But its spatial autocorrelation degree is lower than economic factors because urban datum land price is also influenced by other special local factors,such as population,city infrastructure,land supply,etc.
Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.
