Abstract
Geospatial data is a carrier of information that represents the geography of the real world. Measuring the information contents of geospatial data is always a hot topic in spatial-information science. As the main type of geospatial data, spatial vector data models provide an effective framework for encoding spatial relationships and manipulating spatial data. In particular, the spatial relationship information of vector data is a complicated problem but meaningful to help human beings evaluate the complexity of spatial data and thus guide further analysis. However, existing measures of spatial information usually focus on the ‘disjointed’ relationship in one layer and cannot cover the various spatial relationships within the multi-layered structure of vector data. In this study, a new method is proposed to measure the spatial relationship information of multi-layered vector data. The proposed method focuses on spatial distance and topological relationships and provides quantitative measurements by extending the basic thought of Shannon’s entropy. The influence of any vector feature is modeled by introducing the concept of the energy field, and the energy distribution of one layer is described by an energy map and a weight map. An operational process is also proposed to measure the overall information content. Two experiments are conducted to validate the proposed method. In the experiment with real-life data, the proposed method shows the efficiency of the quantification of spatial relationship information under a multi-layered structure. In another experiment with simulated data, the characteristics and advantages of our method are demonstrated through a comparison with classical measurements.
Highlights
With the development of remote-sensing techniques and geographic information science, more detailed geospatial data have become available to represent this real world
Spatial information, which is an effective tool to measure the complexity of geospatial data, has been widely used in both cartography and geographic information systems (GIS) [1]
This study focuses on the quantitative measurement of spatial relationship information
Summary
With the development of remote-sensing techniques and geographic information science, more detailed geospatial data have become available to represent this real world. Neumann [24] performed instructive work on measuring topological information In his method, vertices are classified according to their neighbor relationships in a graph, which shows all the connections among features in a map, and the entropy is calculated based on the proportions of different types of vertices. The complexity of spatial relationships, which produces information content, relies on the state of knowledge about the d(a)ta [1] It is general(lby)difficult to judge that whether the interior or especially wexhteenriothr eofatFaFtirigpgiubourulreyetge22s.o.(n(oaa)f)bEfEoenndaeeytrruggayryrefiefsieemalldrdoeffroonerroasatipgpconooiiinnnftist;c;i(ad(bbne))treeeinnndee.rcrgSogyoymfi,fpeiienlldlidctfahfotoirisrnaapglialnitpnheee.e.r,spwaetiarel arseolantaiobnlyshtiapkse, themEansetrhgeysafimeled.s are affected by the geometric type of their spatial features. The boundary of a polygon plays an important role in its topological relationships because this boundary is the critical line for the the complexity of spatial relationships, which produces information content, relies on the state of knowledge about the data [1].
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