Many studies on land use change (LUC), using different approaches and models, have yielded good r... more Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research ), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.
Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protes... more Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protest, sports, etc.) with spatial, temporal and semantic information. Previous researches relied much on tag frequency, thus some images which do not have clearly tag indicating the occurrence of social event would be missing in this case. One potential way to address this problem or enhance the event identification is to make more use of the spatial and temporal information. In this chapter, we take into consideration the underlying spatio-temporal pattern of social events. Particularly, the influence of urban land use and road on the occurrence of event is considered. Specifically, with a spatio-temporal cluster detection method, we firstly detected spatio-temporal clusters composed of geotagged images. Among these detected S-T clusters, we furthermore attempted to identify social events in terms of a classification model. Specifically, land use and road were considered to generate new kinds of spatial characteristics used as dependent variables incorporated into the classification model. In addition to this, user characteristics (i.e., the number of images and the number of users), spatial and temporal range of images, and the heterogeneity of temporal distribution of images were considered as the other dependent variables for the classification model. Consequently, with a binary logistic regression (BLR) method, we estimated the categories (i.e., 'event' or 'non-event' one) of the S-T clusters (cases). Experimental results demonstrated the good performance of the method with a total accuracy of 71 %. With the variable selection process of the BLR method, empirical result also indicates that (1) some characteristics (e.g., the distance to the road and the heterogeneity of temporal distribution of images) do not have considerable influence on the occurrence of 'event'; and (2) compared to the other urban land categories (i.e., residential and recreational land), commercial land has a relatively high influence on the occurrence of 'event'.
Many studies on land use change (LUC), using different approaches and models, have yielded good r... more Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research ), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.
Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protes... more Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protest, sports, etc.) with spatial, temporal and semantic information. Previous researches relied much on tag frequency, thus some images which do not have clearly tag indicating the occurrence of social event would be missing in this case. One potential way to address this problem or enhance the event identification is to make more use of the spatial and temporal information. In this chapter, we take into consideration the underlying spatio-temporal pattern of social events. Particularly, the influence of urban land use and road on the occurrence of event is considered. Specifically, with a spatio-temporal cluster detection method, we firstly detected spatio-temporal clusters composed of geotagged images. Among these detected S-T clusters, we furthermore attempted to identify social events in terms of a classification model. Specifically, land use and road were considered to generate new kinds of spatial characteristics used as dependent variables incorporated into the classification model. In addition to this, user characteristics (i.e., the number of images and the number of users), spatial and temporal range of images, and the heterogeneity of temporal distribution of images were considered as the other dependent variables for the classification model. Consequently, with a binary logistic regression (BLR) method, we estimated the categories (i.e., 'event' or 'non-event' one) of the S-T clusters (cases). Experimental results demonstrated the good performance of the method with a total accuracy of 71 %. With the variable selection process of the BLR method, empirical result also indicates that (1) some characteristics (e.g., the distance to the road and the heterogeneity of temporal distribution of images) do not have considerable influence on the occurrence of 'event'; and (2) compared to the other urban land categories (i.e., residential and recreational land), commercial land has a relatively high influence on the occurrence of 'event'.
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