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The results of spatial and temporal investigations lead towards the development of further analysis to investigate the geography and the urban dynamics of the municipality. Especially the major density of SMGI in the built environment may foster the use of advanced analytical methods to identify, classify and interpret the users’ interest toward certain specific spaces. For this purpose, the DB-SCAN algorithm and it slightly modified version called Feature-based DB-SCAN (FB-DBSCAN), integrated in SPATEXT, is used to compute clusters based on the spatial density of points.

As debated in the previous chapter, DB-SCAN algorithm offers major advantages with respect to other clustering algorithms; firstly it is not necessary to know a priori the number of clusters, which also may differ in size and shape. Secondly, it works using two parameters exclusively: the ε (eps) that is the maximum threshold distance for including points in the same cluster, and the minimum number of points (min_pts) that is required to define a cluster. In the study, the goal of the clustering analysis is the identification of the places that attract the major interest of the local community, which in the research is

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 98 proposed to be measured in terms of high density of contributions. Nonetheless, operatively there is no opportunity to establish the preferable value of ε and min_pts before the computation, therefore the DB-SCAN tool is applied iteratively on the SMGI dataset for different measures of the parameters in order to evaluate different results of the clustering. The assessment of clustering results lead toward the identification of the following values, which have proved to be preferable for the purpose of the study: ε = 20 meters and min_pts = 5. Indeed, the ε value, or threshold distance, is able to cover the dimension of a medium-sized fabric, while the min_pts value is set to 5 as a compromise value to avoid false positive in clusters detection and, at the same time, to prevent the dismissal of clusters with a modest participation of users.

The results of clustering analysis with the above set of values enable the identification of 290 clusters within the urban area of Iglesias, with a major concentration near the city center. In addition, two large clusters with an area greater than 50.000 square meters emerge from the analysis, identifying the areas attracting the highest interest by users within the urban context. These areas may represent public spaces, private places, residential and commercial areas. The cluster analysis results are shown in Figure 16.

Figure 16. SMGI cluster analysis. Clusters identified by DB-SCAN.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 99 Nonetheless, in order to find an explanation at the two clusters deserving the major attention of users in the urban environment of Iglesias, a further SMGI extraction is conducted by collecting data from the social networks Foursquare and Instagram Places, respectively. Both these social platforms are location-based social networks, which may provide useful information about POIs in the municipality.

The extraction results are compared in order to detect the common POIs in both the platforms and then to identify the 5 most visited places in the major clusters by means of an overlay analysis. The integration of complementary SMGI dataset demonstrate how these areas concern both the historic center of Iglesias and public space areas. A closer look to the clusters shows that the top cluster includes the historic Cathedral of Santa Chiara, the main avenue for leisure and night life of the municipality, namely Via Matteotti, and two of the main squares of Iglesias. At the same time, the bottom cluster contains the public park of the municipality. The results of the SMGI complementary integration are shown in next figures. The Figure 17 identifies the 5 most important POIs detected in the clusters.

Figure 17. Identification of POIs in the major clusters.

In addition, the next figures 18, 19, 20, 21 and 22 show the identification of the Cathedral of Santa Chiara, the square Piazza La Marmora, the avenue Via Matteoti, the square Piazza Sella and the municipal public garden, respectively. The proposed approach demonstrates how the use of complementary SMGI may be useful to elicit information on places in near real-time, reducing the issues for direct investigations or surveys conducted on the local community. Obviously, the findings of the approach might be further investigated with traditional methods; however, this kind of exploratory analysis may be useful for informing and guiding further analytical efforts.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 100 Figure 18. Identification of the Santa Chiara Cathedral.

Figure 19. Identification of the Piazza La Marmora square.

Figure 20. Identification of the Via Matteotti avenue.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 101 Figure 21. Identification of the Piazza Sella square.

Figure 22. Identification of the municipality public garden.

Along the same vein, the FB-DBSCAN tool is used on the SMGI dataset in order to detect the places of major interest for each user. In fact, the FB-DBSCAN algorithm processes the dataset after performing a selection for attribute on the sample, in this case the users. This way, the algorithm computes clusters by processing only points related to a specific user for each iteration, offering opportunities to develop more specific analysis on the users’ behavior.

The analysis through FB-DBSCAN with the parameters eps = 20 meters and min_pts = 5 identify 368 clusters concerning 266 users. In this case, the number of identified clusters is higher than the value obtained in the previous analysis by means of DB-SCAN, but the clusters’ sizes is notably smaller, focusing on specific places or fabrics within the municipality. Each cluster identified through the FB-DBSCAN tool belonged to the contributions of a single user, and could be considered representative of a specific use regarding residence, work or leisure activities. The results of the clustering analysis performed through the SPATEXT tool FB-DBSCAN are shown in Figure 23.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 102 Figure 23. SMGI cluster analysis. Clusters identified by FB-DBSCAN.

The real clusters utilization by users may be discovered analyzing several parameters related to spatial and temporal characteristics, as well as by integrating further spatial information. In this case, the aim of the study is twofold: 1) identify the users’ residential location, and consecutively 2) detect eventual not mapped buildings in the official information. In order to accomplish the analysis, the latest official buildings dataset from the Regional SDI is integrated.

This official dataset is selected in order to check the consistency of the clusters’ location with the urban fabrics, easing the identification of suitable parameters to detect residential clusters. As a matter of fact, the clusters related to a specific land use, in this case residential use, may expose similar patterns for certain characteristics, such as number of intersections among clusters, temporal span among contributions, number of contributions and density of contributions, to name few, paving the way to the identification of common patterns for classification.

The approach proposed in the research identifies six different parameters concerning the cluster itself or the included contributions, as described in Table 8.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 103

Parameters Description Units of measure

Cluster Centroid The overlap of the cluster’s centroid with an official building footprint is estimated

Boolean Contributions Centroid The overlap of the cluster’s contributions centroid with an official

building footprint is estimated

Boolean Number of Contributions The total number of contributions contained in the cluster is

estimated

Number of contributions Cluster Intersections The total number of intersections between the cluster’s shape

with other clusters

Number of intersections Cluster Density The ratio between the cluster’s area and the number of contained

contributions

Square meters Time Span Among Photos The time passed between the first contribution and the last one in

the cluster

Days Table 8. Parameters used to identify residential clusters.

The above parameters are strictly related to the nature of the Instagram SMGI dataset and are selected specifically in order to ease the identification of dwelling areas from the single user clusters. Nevertheless, the parameters may be modified in order to search for other types of objects within any set of clusters. As a matter of fact, the Instagram SMGI data model provides information related to different dimensions, namely spatial, temporal, user and textual, paving the way for the identification of other sets of parameters, suitable to identify several urban uses and locations, such as night locals, schools, commercial services or working places, to name a few. For example, in order to identify night locals, the set of parameters might be modified to include exclusively clusters containing SMGI posted mostly during the night hours and by an established minimum number of users. Similarly, the working locals might be identified selecting clusters that show most of the contributions within specific time intervals during workdays and a lack of contribution during offices closure days. The temporal patterns of SMGI may be considered representative of different urban uses and in literature many studies rely upon the temporal dimension of contributions to investigate the urban land uses (Frias-Martinez et al., 2012; Torres and Costa, 2014; Silva et al., 2013 B).

The values of the six parameters are estimated for each cluster, while several combinations of the values are iteratively evaluated in order to identify exclusively the residential clusters. The following set of values results as the most appropriate to classify a cluster as residential in the study area: Cluster Centroid and Contributions Centroid have to be 1 (yes), while Number of Contributions and Time Span Among Photos have to present the highest values among clusters of the same user, or the values have to be higher than 10 and 30, respectively. Finally, Cluster Intersections has to be equal or lower than 2, while Cluster Density has to be higher than 4. The above parameters allow the identification of 47 residential clusters, which are confirmed by an overlay analysis with satellite imagery in GIS environment.

Afterwards, the same set of parameters is used to identify potential missing buildings in the official dataset by setting to 0 (no) the values of Cluster Centroid and Contributions Centroid, while leaving unchanged the

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 104 other parameters values. Indeed, the values of Number of Contributions, Cluster Intersections, Time Span among Photos and Cluster Density, may be considered as a sort of residential fabrics footprint among clusters and are used for the investigation. The parameters’ values used to identify the missing buildings in A-GI are listed in Table 9.

Parameters Description Value

Cluster Centroid The overlap of the cluster’s centroid with an official building footprint is estimated

0 [boolean]

Contributions Centroid The overlap of the cluster’s contributions centroid with an official building footprint is estimated

0 [boolean]

Number of Contributions The total number of contributions contained in the cluster is estimated

> 10 contributions Cluster Intersections The total number of intersections between the cluster’s shape

with other clusters

<= 2 Cluster Density The ratio between the cluster’s area and the number of contained

contributions

=> 4 sq. meters Time Span Among Photos The time passed between the first contribution and the last one in

the cluster

> 30 days Table 9. Values of parameters for identifying residential clusters.

The analysis identifies 40 clusters, which are then visually assessed through satellite imagery to confirm the presence of not mapped buildings in A-GI. The visual assessment detects 9 not mapped buildings.

Nevertheless, at the same time the other 31 identified clusters are confirmed as residential areas, but in this case the buildings are already mapped in A-GI. This issue may depend upon the lack of tolerance in the analysis performed during the comparison of Cluster Centroid and Contributions Centroid parameters with the official buildings dataset. An example of the analysis results is provided in Figure 24, wherein six different clusters (i.e. A, B, C, D, E and F), their barycenter, the existing buildings footprints from the official dataset, the main roads network, and the Instagram SMGI dataset are shown.

Figure 24. Results of clusters investigation in the Iglesias municipality.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 105 In this example, the manual investigation through the Google Maps satellite image enables the detection of two buildings which are not mapped in the official dataset, namely cluster B and D. At the same time, the visual assessment confirmed the building presence in cluster A, C, E and F. This example demonstrates the potentialities of Instagram SMGI to elicit information related to geography of places, and also shows how this information may be potentially used as a support for the update and the integration of official datasets.

6.6 Discussion

This chapter discusses a number of example case studies carried on at different geographic scales, in order to investigate both the local communities’ perceptions on relevant topics for spatial planning and the geography of places. Altogether, these examples contribute to demonstrate how SMGI may be used to elicit information, not only about the physical geography of places, to integrate existing A-GI, but also to express the perceptions of places and issues in time and spaces by the involved community, adding a pluralist perspective for spatial planning and decision-making processes. As a matter of fact, SMGI may be integrated with A-GI and used to understand people perceptions, contributing to define a pluralist model of local identity. The results of these first experiences offer an overview on potential uses of SMGI to investigate what people observe, evaluate, and how they behave in space and time. However, the underlying endeavor of the case studies is to test both the novel SMGI Analytics framework and the SPATEXT tools, which may ease the access to this novel data source to extract meaningful knowledge for spatial planning. The discussed case studies confirm the proposed assumptions concerning the SMGI Analytics framework and the SPATEXT tool. The novel methodological approach and the ad-hoc developed instruments may be able to foster the use of SMGI into practices, easing the integration of this experiential knowledge with the available official information.

The novel clustering approaches for identifying areas and POIs are evaluated in practices, demonstrating to be appropriate to deal with SMGI, enabling the elicitation of useful information. What appeared clear from the development, as well as, from the usage of SPATEXT, is that any SMGI source is characterized by a specific data model and by different rates in geographic diffusion. Therefore, different combinations of analytical approaches are required to interpret the results and consecutively the local context, appropriately. However, exploiting SMGI from different popular social media platforms demonstrate the capability of these sources to provide interesting results both for detecting changes in topography, as in the case of Instagram photos, as well as for investigating users perceptions and opinions, as in YouTube and Twitter cases. Finally, the discussed example case studies are useful to formalize the different stages of the SMGI Analytics framework, which is then applied on a complex case study strictly related to spatial planning analysis and decision-making in the municipality of Cagliari in Sardinia.

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 106

CHAPTER 7

SMGI ANALYTICS FRAMEWORK: PUBLIC SPACES ANALYSIS

7.1 Introduction

The results achieved from the early case studies demonstrate that the wealth of information enclosed in SMGI might be proficiently used to investigate the concerns and the attentions of people toward places, as well as, the users’ behaviors and movements in space and time, fostering opportunities for gaining insights about urban dynamics and users preferences. Nonetheless, the case studies stress how different analytical approaches, integrating multiple social networks data with official information, may be required in order to investigate the local contexts appropriately. Several SMGI Analytics stages were tested and evaluated dealing with SMGI from different sources and at different geographic scales, exposing a number of potential approaches to elicit knowledge in order to explain examined phenomena. However, the obtained findings highlight the differences arising from the use of different social media platforms for developing specific spatial analyses.

The Instagram social network allows the extraction of massive georeferenced data for any time period and location and exposes a wide diffusion among users in the Sardinia Region (Italy). Therefore, this social media platform appears as the most suitable to enable SMGI Analytics in order depict the users’ dynamics and preferences, enabling the investigation of spatial phenomena in urban environment at the local scale.

Moreover, the point data model of Instagram SMGI is suitable for developing novel clustering methods, which may take into account the spatial, temporal and user distribution of contributions, easing the investigation of the places’ geography. As a matter of fact, the proposed clustering analyses, relying on the SMGI density, may allow the identification of public and private areas of interest, as well as, of specific POIs in the urban environment.

In the light of these considerations, this chapter discusses the SMGI Analytics framework application on an Instagram SMGI dataset in order to investigate the public spaces of the Poetto beach and the Regional Park of Molentargius in the municipality of Cagliari, Sardinia (Italy). Operatively, the study is based upon all the stages identified for the SMGI Analytics framework, although the opportunities arising from textual analyses are strongly limited by the lack of structured textual contents in Instagram SMGI, and the step is neglected in this study. Actually, the case study is carried out through the following steps:

1. data collection;

2. explorative spatial-temporal analyses and A-GI integration;

3. spatial-temporal-user cluster analyses;

Social Media Geographic Information (SMGI): opportunities for spatial planning and governance. 107 4. geodemographic classification;

5. user profiling;

6. multi-dimensional analyses on public spaces.

In particular, the analysis of SMGI user component plays a more important role in this study, providing opportunities to analyze the urban dynamics and preferences of specific users groups in space and time.

Hence, an ad-hoc geodemographic classification (Webber and Craig, 1978; Sleight, 1997) of the Sardinian territory is conducted, relying on the official census data provided by the ISTAT. The geodemographic classification findings are then discussed and integrated with the clustering analyses results to achieve the Sardinia population profiling. Thus, the final results are used for developing a number of multi-dimensional analyses regarding concerns and preferences of the different identified population groups in several locations within the study area.

7.2 Data collection

The data collection is carried out by the SPATEXT Instagram extractor tool setting the spatial query on the public spaces of the Poetto beach and the Regional Park of Molentargius, and the temporal query on a one year period (26 January 2014 to 25 January 2015). The data extraction results in a one year sample of 34,776 geotagged photos from 8,350 users concerning areas in the Cagliari municipality and partially in the

The data collection is carried out by the SPATEXT Instagram extractor tool setting the spatial query on the public spaces of the Poetto beach and the Regional Park of Molentargius, and the temporal query on a one year period (26 January 2014 to 25 January 2015). The data extraction results in a one year sample of 34,776 geotagged photos from 8,350 users concerning areas in the Cagliari municipality and partially in the