Abstract: With the continued expansion and population growth of development zone, the study on residents' living distribution is becoming more and more important and gets increased emphasis by researchers. With the development and improvement of market system of real estate, urban residents' housing consumption for individual use has become the main body of real estate market. So, their individual characteristics have direct influences on their residential location-selections. Choosing age, earning, education background, geo-relationship, family structure and vocation as indexes, based on the large-scale questionnaire survey of residents' individual characteristics in Kunshan Development Zone, the study explores the spatial living distribution of different groups of residents with the method of ecological factor analysis and spatial autocorrelation analysis. The thesis tries to identify all the individual characteristics into two main integrated factors which are local social attachment degree and economic capability. The residents are divided into five representative groups accor?d?ing to the two main integrated factors, including the groups of high-economic-capability (type Ⅰ), medium-economic-capability and low-attachment-degree (typeⅡ), medium-economic-capability and high-attachment-degree (type Ⅲ), low-economic-capability and high-attachment-degree (type Ⅳ), low-economic-capability and low-attachment-degree (type Ⅴ). The paper reveals that types Ⅰ, II, Ⅲ and IV people have the most significant living aggregation, but the spatial distribution of type Ⅴ people is random. From the view of local spatial autocorrelation, the paper finds that type Ⅰ people obviously clusters to the corn areas, type Ⅲ people cluster to the old areas, type Ⅱ people cluster to the new areas where have perfect facilities closing to the industrial district, type Ⅳ people cluster to city village and marginal division where human settlements is poor. Type Ⅴ people has the larger proportion in city village, marginal division and farmer setting community.