关键词: 城郊, 聚落景观, 集聚算法, 尺度

Abstract:

The spatial pattern of high level concentration on settlement landscape in suburban is the most obvious characteristics of urbanization in the outskirts. The present spatial agglomeration methods for landscape are usually used without selection, and they are also lack of comparability. Therefore, this study proposes 4 different analysis of spatial clustering algorithms in a typical area to describe the agglomeration of settlements. And then a simple application in this typical area which realized by one of the 4 analyses is shown. The results reflect that: 1) The landscape aggregation index (CONTAG) is adapted to distinguishing similar continuous large patches and small fragments which are in different types, and the search radius is relatively limited; nuclear density algorithm is adapted to identifying the macro clustering groups, and it’s not usually used in a small spatial scale; spatial correlation algorithm is suitable for spatially locating the specific elements, and the spatial gradual changes are not seen; Ripley's L function is suitable for recognizing different spatial scales to determine the search radius, but the result is rather a chart than a map; 2) Based on the results of the nuclear density algorithm, three large groups are draw on the settlement landscape in the study area, respectively named "government business center settlement groups" "Feng Wei new settlement group" and "Tourism Resort settlement group", the division mode is consistent with the direction of future development for each economic sector in the study area. Landscape planning itself is a subjective process and may not have the only correct process. Quantification of spatial agglomeration is one of the important ways to study the settlement geography from qualitative methods to quantitative methods. Therefore, calculation results may clearly reflect the rationality of the planning, which is currently in need in the landscape research. The quantitative calculation cannot replace the qualitative description, and the diversity of methods leads to a more perfect planning. It goes without saying that more practicable and objective spatial agglomeration algorithms are looked forward to be applied in the settlement landscape studies.

Key words: suburban, settlement landscape, clustering algorithm, scale