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Outline

Title
Abstract
Figures
Introduction
Analysis Rank
Individual Data Sets
Individual Results
Analysis
Group Results
Pool Results
Results Averaged
Group Result
Aggregation Strategies for Cognitive Mapping Data
The Experimental Data
Results
Conclusions
References

Aggregation Issues in Cognitive Mapping

Profile image of Rob KitchinRob Kitchin
https://doi.org/10.1111/0033-0124.00076
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Abstract

Cognitive mapping involves the description of the way individuals store and process geographic information. Typically, cognitive mapping data are derived from individual responses and then analyzed in one of three ways: (1) the individual data sets are analyzed separately and only pooled for comparison (disaggregation); (2) the individual data sets are averaged and then analyzed (collective aggregation); or (3) the individual data sets are analyzed and the results averaged (individual aggregation). This paper compares the latter two aggregation strategies for analyzing cognitive mapping data using data collected in a large-scale study of students' configurational knowledge of the city of Swansea in the United Kingdom. It is contended that the aggregation strategy adopted will have a fundamental effect upon the conclusions drawn from a study.

Figures (7)
Figure 1: Strategies of analysis. 270 Volume 49, Number 3, August 1997
Figure 1: Strategies of analysis. 270 Volume 49, Number 3, August 1997
Figure 2: The tests used to investigate aggregation. The data used were collected as part of a larger study assessing 170 students’ configurational knowledge of the Swansea region in the United Kingdom. In this larger study, students were divided into 6 groups who each completed 4 tests from a total of 13 tests. In all, 52 students were asked to locate 25 places in the region using two different spatial cued response (SCR) tests (Fig. 2). In the first (SCR1), 19 students were given only the locations of the Geography De- partment at the University of Wales Swansea and the bus station to guide their placements. In the second (SCR2), 33 students were also given the coastline to guide their responses. Given that respondents were matched for educational ability and familiarity (all had been resident in Swansea for 9-10 weeks), one might expect the responses to be more accurate in the second test where more spatial information was provided. ‘lo tully explore any potential aggregation effects caused by the aggregation strategy used, the two sets of experimental data (SCR1 and SCR2) were analyzed at four different levels. The resulting sub-data sets are differentiated in terms of the number of locations they contain (Table 1). The data sets are progressively ordered with data of increasing familiarity re- moved at each level. Data set A contains data on the cognitive locations of all 25 places. In data set B, the locations of all places reported as “guessed” by the respondents were eliminated. In data set C, those places that scored a familiar rating less than 2 (unknown or low familiarity places) were excluded; and in data set D, those places scoring a familiar rating less than 4 (un- known, low, and medium familiarity places) were excluded to leave those places which were very familiar. All the data sets were analyzed using bidimensional regression, which is essen- tially ordinary least squares regression extended to two dimensions (Tobler 1965), with cognized locations bidimensionally regressed into real world locations. This was performed using the CMAP package (Kitchin 1994b).
Figure 2: The tests used to investigate aggregation. The data used were collected as part of a larger study assessing 170 students’ configurational knowledge of the Swansea region in the United Kingdom. In this larger study, students were divided into 6 groups who each completed 4 tests from a total of 13 tests. In all, 52 students were asked to locate 25 places in the region using two different spatial cued response (SCR) tests (Fig. 2). In the first (SCR1), 19 students were given only the locations of the Geography De- partment at the University of Wales Swansea and the bus station to guide their placements. In the second (SCR2), 33 students were also given the coastline to guide their responses. Given that respondents were matched for educational ability and familiarity (all had been resident in Swansea for 9-10 weeks), one might expect the responses to be more accurate in the second test where more spatial information was provided. ‘lo tully explore any potential aggregation effects caused by the aggregation strategy used, the two sets of experimental data (SCR1 and SCR2) were analyzed at four different levels. The resulting sub-data sets are differentiated in terms of the number of locations they contain (Table 1). The data sets are progressively ordered with data of increasing familiarity re- moved at each level. Data set A contains data on the cognitive locations of all 25 places. In data set B, the locations of all places reported as “guessed” by the respondents were eliminated. In data set C, those places that scored a familiar rating less than 2 (unknown or low familiarity places) were excluded; and in data set D, those places scoring a familiar rating less than 4 (un- known, low, and medium familiarity places) were excluded to leave those places which were very familiar. All the data sets were analyzed using bidimensional regression, which is essen- tially ordinary least squares regression extended to two dimensions (Tobler 1965), with cognized locations bidimensionally regressed into real world locations. This was performed using the CMAP package (Kitchin 1994b).
Table 1 Experimental Data Sets
Table 1 Experimental Data Sets
Table 2 Spatial Cued Response Test 1 Aggregated Results for Four Data Sets CA - data aggregated IA - individual aggregated med - median st d- standard deviation
Table 2 Spatial Cued Response Test 1 Aggregated Results for Four Data Sets CA - data aggregated IA - individual aggregated med - median st d- standard deviation
Table 3 Spatial Cued Response Test 2 Aggregated Results for Four Data Sets CA - data aggregated IA - individual aggregated med - median st d- standard deviation
Table 3 Spatial Cued Response Test 2 Aggregated Results for Four Data Sets CA - data aggregated IA - individual aggregated med - median st d- standard deviation
Figure 3: Comparing selected bidimensional regression results across sub-data sets. 276 Volume 49, Number 3, August 1997
Figure 3: Comparing selected bidimensional regression results across sub-data sets. 276 Volume 49, Number 3, August 1997
Figure 4: A-squared values from the spatial cured response tests. cognition. Place cognition is the consensual view of where each individual place is located. Through collective aggregation, a group’s un- derlying, shared cognitive map knowledge can be investigated, with the unique individual de- viations removed. It is argued that any remain- ing systematic distortions will be part of normal processing and part of a group’s cognitive map However, although the above results suggest that the collective aggregation strategy needs to be used with caution when examining cognition, the strategy does allow groups of individuals to be compared on the basis of their shared place
Figure 4: A-squared values from the spatial cured response tests. cognition. Place cognition is the consensual view of where each individual place is located. Through collective aggregation, a group’s un- derlying, shared cognitive map knowledge can be investigated, with the unique individual de- viations removed. It is argued that any remain- ing systematic distortions will be part of normal processing and part of a group’s cognitive map However, although the above results suggest that the collective aggregation strategy needs to be used with caution when examining cognition, the strategy does allow groups of individuals to be compared on the basis of their shared place

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