Focusing on historic, cultural and old villages in Guangdong Province, the authors extract and synthesize features and information on sponge measures applied by local villages. Based on aerial map on Google Earth, the pattern of the settlement and water system are observed and categorized. Based on 30 m DEM picture of Guangdong Province in ArcGIS, landscape and the graded water system of the whole province and the elevations of all villages are extracted, followed by inundation analysis. The level of the rain intensity in different parts of the province is also considered. 5 features are coded and input into SPSS. By hierarchical cluster analysis, the villages are classified. The study generalizes the sponge measures of all classifications and concludes with 4 kinds of sponge measures in villages in Lingnan area: terrain utilization, slowed runoff through impoundment, strategically directed runoff, and forced drainage. Suggestions are proposed to promote the application of traditional ecophronesis in modern urban practices in Linghan area.

A qualitative method is presented for geographic information retrieval (GIR) to support qualitative representation, semantic matching, reasoning and ranking. The novel approach can avoid semantic information lost in current quantitative GIR methods. Information in documents and user queries are represented by propositional logic, which considers the thematic and geographic semantics synthetically. The similarity between documents and queries can be divided into thematic similarity and geographic similarity. The former is calculated by the weighted distance of proposition keywords in domain ontology, and the latter is further divided into conceptual similarity and location similarity which are measured by geo-ontology and spatial semantic respectively. Represented by propositions and information units, the similarity measurement takes evidence theory and fuzzy logic to obtain a general similarity from all sub similarities. This novel method retrieves qualitative geographic information from web and ranks documents semantically, which is consistent with commonsense, and thus can improve the efficiency of geographic information retrieval technology.