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Geography and cartography computing

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Novel methods to demarcate urban house submarket - cluster analysis with spatially varying relationships between house value and attributes
Author(s): Danlin Yu, Jingyuan Yin, Feiyue Ye
Source: IET International Conference on Smart and Sustainable City (ICSSC 2011), 2011, p. 41 - 41
Type: ConferencePaper
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In urban house market studies, urban housing market can be divided into a series of submarkets. Usually, submarkets are identified with either geographic locations or housing structural characteristics, or some combination of both. In this study, we propose an alternative to identify urban housing submarkets. Instead of using house characteristics or locations, we use the relationships obtained through a geographically weighted hedonic regression (GWHR) model. In particular, we apply a K-means classification on the coefficients obtained via GWHR to identify different submarkets. Data from the City of Milwaukee are used to test the model and procedure. Comparison of a regular cluster analysis using housing structural and neighborhood socioeconomic information and the proposed procedure is conducted in terms of prediction accuracy. The analytical results suggest that hedonic regression on demarcated submarkets is better than a uniform market, and our proposed method yields more reasonable result than the ones using raw data. (6 pages)

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Geographic information analysis - past and trends
Author(s): Danlin Yu, Jingyuan Yin, Feiyue Ye
Source: IET International Conference on Smart and Sustainable City (ICSSC 2011), 2011, p. 35 - 35
Type: ConferencePaper
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Effectively analyzing geographic information has attracted renewed attention in a wide range of fields, especially in urban studies. The specific characteristics of geographic information make the analysis of geographic information rather unique. In this review, we discuss the unique characteristics of geographic information and introduce some classical and new techniques in analyzing geographic information. In particular, we pay specific attention to analyzing spatial heterogeneity that inherently exists in geographic information, specifically regressed relationships using geographic information. The extension of analyzing spatial heterogeneity of geographic information in a temporal context is discussed as well. This review echoes a belief that one of the major tasks of analyzing geographic information is to develop new methods to help understand the ever increasing volume of geographic information better. We hope this review serves as a start for interested scholars to energetically seek better analytical techniques to get more insights of what geographic information can offer. (6 pages)

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