Flickr Photos Analysis for Beach Tourism Management in Bantul Regency, Indonesia: Popularity and Tourist Attractions

Photos shared by social media users act as an approach in identifying tourist activity. Popular tourist attractions are judged based on the large number of photos or high photo density. In Bantul Regency, Indonesia, beaches have diverse attractions which tourists can enjoy and immortalize through photos. Analyzing the contents of photos on Flickr provides information on the type(s) of beaches or coastal attractions preferred by tourists. This study examined the availability of geotagged Flickr photos to assist in making relevant beach tourism management policies. It employed pattern analysis with the average nearest neighbor, density analysis with kernel density estimation, image content analysis with tourist attraction as the variable, and overlay analysis to formulate recommendations for beach tourism management based on the popularity level of the attractions. The results indicate that each of the local beaches offers different attractions with varying popularity levels and that natural beauty is the main feature attracting tourists to visit all beaches, except Baros. Based on the pattern analysis, the Flickr photos are clustered on several beaches of high popularity, such as Parangtritis, Baros, Depok, and Cemara Sewu. By using geotagged Flickr photo data and refers to the concept of tourism supply and demand, recommendations for developing the attractive features on these beaches have been compiled according to their respective themes and popularity levels to target specific tourist market segments and design integrated tour or travel packages

Leveraging Geotagged Social Media to Monitor Spatial Behavior During Population Movements Triggered by Hurricanes

In a world of increased mobility and interconnectedness, the study of spatial behavior becomes more relevant than ever. However, multiple researchers have highlighted that the understanding of these dynamic processes has reached a bottleneck derived from the rigidity of traditional spatial behavior inquiry methods and the unavailability of trustworthy and relevant information. These difficulties are even more prominent during emergencies and disasters as these events often create scenarios where spatial behavior does not follow regular and logical patterns and where conventional mobility datasets are often skewed or not existent. Thus, many scholars working within the spatial behavior sub-discipline are pursuing innovative data collection methods to deepen the understanding of human spatial behavior. Researchers see digital geospatial trace data, also known as passive citizen sensor data, as one of the most promising opportunities to develop and test new hypotheses on spatial behavior. Nevertheless, the application of these new methods has not been fully explored within the hazard/disaster discipline for spatial behavior purposes under stressed situations. This dissertation investigates the suitability of geotagged social media (Twitter) as an innovative approach for the study of spatial behavior of people in stressed contexts and responds to three main research questions: 1) How well do geotagged social media estimate hurricane evacuation compliance? 2) To what extent is geotagged social media amenable for determining hurricane evacuation behavior? 3) How suitable is geotagged social media to evaluate post-disaster displacement and tourist flows? The dissertation therefore not only attempts to develop a new method to estimate the number of movements associated with the different stages of an emergency but also tries to answer long-standing questions about the response of different population sub-groups (residential status, gender, age, race/ethnicity) before, during, and after hurricanes. Results confirm the potential of geotagged social media to tackle some of the deficiencies of traditional approaches, particularly offering more timely, dynamic, and affordable information about the evacuation and post-disaster population movements. In addition, results demonstrate that the Twitter-based approach complements survey-based methods as it permits accessing underrepresented groups in traditional approaches such as the young, short-term residents, and racial/ethnic minorities. Although the representativeness of Twitter samples is still debatable and needs further research, this method to investigate emergency-triggered population movements can ultimately improve our understanding of the response and recovery phases of a disaster

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.Agencia Estatal de Investigación | Ref. PID2020-116040RB-I00Universidade de Vigo/CISU

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Spatiotemporal Analysis of Human Mobility based on Land Use Types in the Greater Toronto Area during COVID-19 Pandemic

The 2019 Coronavirus disease COVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It undoubtedly poses a huge challenge in terms of public health and social impact worldwide. The Ontario government implemented a series of non-pharmaceutical interventions (NPIs) prior to vaccination to prevent large-scale outbreaks in the Great Toronto Area (GTA), which is the most densely populated region in Ontario. Detecting and analyzing human mobility during the pandemic can help decision makers assess the effectiveness of policy implementation, in order to better respond to similar events in the future. Geotagged Twitter data serves as an important source of volunteered geographic information (VGI). Anonymized geotagged tweet in the GTA in 2020 using the Twitter Academic API are used to analyze inner-city human mobility. The results provide a longer-term insight into how human activity is affected by the pandemic as well as government orders. In this thesis, human mobility spatiotemporal patterns in the GTA are found to be close to patterns founded in the previous studies. People are affected more by the severeness of the first outbreak. More people stay at home rather than in commercial areas, schools, and workplaces. Human mobility in open spaces is affected by seasons besides policy effects. Human mobility in utility and transportation areas is related to the properties of the areas they connect. Most of the policies received significant reflections within one week of release, but milder policies resulted in insignificant human mobility changes. Human mobility patterns in most land use types have moderate correlation with the Google Community Mobility Report. Even so, some limitations still exist

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

Assessing post-disaster recovery using sentiment analysis: The case of L’Aquila, Italy

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary’s exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitter-based SA on their anniversaries