# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
## 1 Introduction
One of the main problems faced today in spatial analysis is the detection of urban or territorial areas where a specific phenomenon refers to hot spot analysis. Formally, areas of the study area that have a high/low density of occurrences caused by the phenomenon under consideration are classified as hot/cold patches. Statistical indices, such as Getis-Ord Gi* statistic [14] and Local Index of Spatial Autocorrelation (LISA) [15], are used to detect hot and cold spots; Gi* evaluates the spatial association of a spatial entity with surrounded ones within a specified distance of a single point. LISA statistics detect spatial autocorrelation between neighboring features, measuring the degree of similarity of data points close to a given data point within a specified threshold distance. LISA is used in [16] for drought hot spot detection in northern Bangladesh. Kernel Density Estimation (KDE) [17; 18] is another well-known hot and cold spot detection technique; by identifying hot/cold spots as zones with high/low-intensity peaks relative to the surrounding areas, it provides a continuous surface representing the geographical distribution of the intensity of the phenomena. These methods, while distinguishing hot and cold spots from areas in which the intensity of the phenomenon appears normal, are not able to accurately detect the location and extent of hot and cold spots. To overcome this drawback, some authors propose partitive cluster algorithms to detect the extension and the shape of hot and cold spots. K-means [19] is implemented in GIS environments to detect hot and cold spots in crime [20; 21; 22] and fire analysis [23; 24]. Recently, K-medoids [25] was applied to detect hot spots in crime [26] and disease analysis [27], and FCM [8; 28] was used to detect hot spots in crime analysis [29; 30] and road traffic crashes [31]. One of the main defects of hot spot detection methods based on partitive clustering algorithms is the impossibility of detecting the shape of hot and cold spots. In order to accurately detect the shape of the hot spots in [32], a method of hot spot detection was adopted which makes use of the density-based Fast DBSCAN clustering algorithm [33]; in [34], a hybrid clustering combining a self-organizing map and a hierarchical clustering method is applied in a GIS platform to detect service levels of urban streets from GPS flow speeds data points. Recently EFCM was implemented in a GIS framework in [35] to detect hot spots in fire analysis and in [36] to detect the spatial distribution and the evolution of hot spots in disease analysis. The shape of a hotspot is approximated by a circle on the map. This approach represents a trade-off between the computational speed of hot and cold spot detection methods based on partitive clustering methods and the accuracy of approaches using density-based clustering algorithms in identifying the form of hot and cold areas. These hot and cold spot detection methods are performed on data points located on the map detecting those areas with a high and low density and intensity of data points. In this paper, we propose a different hot and cold spot detection approach in which areas detected as hot/cold spots are those in which a feeling of unease and discouragement expressed by citizens on social media prevails (it is not very present) and persists over time. Social data can provide an extensive source of information useful for hot and cold spot analysis, both because they refer to the perceptions and moods of the citizens involved in the phenomenon analyzed and because they represent a massive data source that allows you to analyze how a phenomenon spreads and how it evolves. In addition, in many spatial analysis problems, it is useful to aggregate the information by sub-zone in which the study area is partitioned in order to classify these sub-zones on the basis of the impact of the phenomenon analyzed.