### Table 1: Human interpretation of the spatial distribution of building objects in the test maps. Even distribution of

"... In PAGE 7: ...Table1 ). Table 1: Human interpretation of the spatial distribution of building objects in the test maps.... In PAGE 8: ... That is, an analytical measure that is close to zero for all test maps corresponds well to the judgement of the evaluators in terms of information amount. In Table1 we defined four groups depending on the spatial distribution of information (building objects). We computed the mean and standard deviation of the spatial distribution measures for these four groups (Table 3).... In PAGE 8: ... Table 3: Normalised entropy values for spatial distribution of objects (HISD-OBJ) and spatial distribution of points (HISD-POI). The values are described for the groups defined in Table1 using the mean value and standard deviation. Even distribution of buildings in the map.... ..."

### Table 4 shows the results of the regression related to the standard deviation of the frequency of OR losses. The interpretation is straightforward. Roughly spoken, the pattern of results is equal to that of

2005

"... In PAGE 16: ...1831 0.0002 Table4 : Parameter and statistical values of the regression with the means of the frequency of OR losses. big proportion of the variability in the frequency of operational loss per week, as we can conclude from the high value of R2 (0.... ..."

### Table 1 Topological interpretation of the eight base relations of RCC-8. i( ) speci es the topological interior of a spatial region, the topological closure.

1999

"... In PAGE 3: ... Exactly one of these relations holds between any two spatial regions. These relations can be given a straightforward topological interpretation in terms of point-set topology (see Table1 ), which is almost the same as the semantics for the topological rela- tions given by Egenhofer [12] (though Egenhofer places stronger constraints on the domain of regions, e.g.... In PAGE 7: ...oth regions must not be empty, i.e., the complements of both X and Y are not equal to the universe. In the same way all topological constraints corresponding to the RCC-8 relations (see Table1 ) can be written as constraints of the form (m = U) and (e 6 = U), where m and e are set-theoretic expressions, denoted as model constraints and entailment constraints, respectively [2]. In the above example, X \ Y is the model constraint and X and Y are the entailment constraints.... ..."

Cited by 91

### Table 4. Spatial PBM estimation results Sub-

2006

"... In PAGE 15: ... observations (one per quarter) for each commune, using roughly 5,000 observations at each spatial scale to estimate the probabilities associated with each market regime at each of the three spatial scales ( Table4 ).9 Striking differences emerge in spatial market integration measured at different scales of analysis.... ..."

### Table 1: Basic classes of data interpretation operations.

1998

"... In PAGE 5: ...hat correspond to a measurement (e.g., area, length) characterizing their zones. Table1 summarizes the basic classes of data interpretation operations accompanied by representative examples (Aronoff 1989, Tomlin 1990). Notice that data interpretation operations may be combined to compose one or more procedures (a procedure is any finite sequence of one or more operations that are applied to meaningful data with deliberate intent; Tomlin 1990) and accomplish a composite task posed by the spatial decision making process.... In PAGE 10: ...2.1 Location Properties for Surfaces Queries on location properties for surfaces, such as information regarding the height or slope of a given point ( Table1 ), are commonly posed on geographic databases. SAM organizing MBR approximations may support the retrieval of location properties, if original surface data (i.... ..."

Cited by 3

### Table 1: Classification of spatial index structures.

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB The accuracy of the filtering step is maximized by minimizing the deviation of the approximation from the original object. We measure this deviation by the false area of the approximation normalized to the area of the original object.... In PAGE 14: ... We measure this deviation by the false area of the approximation normalized to the area of the original object. Table1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively.... In PAGE 19: ... Certain mea- sures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly affect the performance of indexes using it.... ..."

### Table 1: Classification of spatial index structures.

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB The accuracy of the ltering step is maximized by minimizing the deviation of the approximation from the original object. We measure this deviation by the false area of the approximation normalized to the area of the original object.... In PAGE 14: ... We measure this deviation by the false area of the approximation normalized to the area of the original object. Table1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively.... In PAGE 19: ... Certain measures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly a ect the performance of indexes using it.... ..."

### Table 1: Classification of spatial index structures.

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB The accuracy of the filtering step is maximized by minimizing the deviation of the approximation from the original object. We measure this deviation by the false area of the approximation normalized to the area of the original object.... In PAGE 14: ... We measure this deviation by the false area of the approximation normalized to the area of the original object. Table1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively.... In PAGE 19: ... Certain measures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly affect the performance of indexes using it.... ..."

### Table 1: Classification of spatial index structures.

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB The accuracy of the ltering step is maximized by minimizing the deviation of the approximation from the original object. We measure this deviation by the false area of the approximation normalized to the area of the original object.... In PAGE 14: ... We measure this deviation by the false area of the approximation normalized to the area of the original object. Table1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively.... In PAGE 19: ... Certain measures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly a ect the performance of indexes using it.... ..."

### Table 1: Classification of spatial index structures.

"... In PAGE 14: ...64 0.89 Table1 : False area of the BB The accuracy of the ltering step is maximized by minimizing the deviation of the approximation from the original object. We measure this deviation by the false area of the approximation normalized to the area of the original object.... In PAGE 14: ... We measure this deviation by the false area of the approximation normalized to the area of the original object. Table1 shows impressively that real cartography objects are only roughly approximated by BBs. In this table, fa is the average false area; min and max denote the minimum and the maximum false area in the map, respectively.... In PAGE 19: ... Certain measures are supported to maintain that each data object is totally included in a subspace. Table1 groups various index structures according to the techniques used to handle non-zero sized spatial objects. Each of the above approaches has its own strengths and weaknesses, which directly a ect the performance of indexes using it.... ..."