The development of tourist attractions is now highly influenced by social media. The speed at which information can be disseminated via the Internet has become an essential factor in enabling distinct tourist attractions to potentially gain high popularity in a relatively short time. This condition was not as prevalent several years ago when tourism promotion remained limited to a certain kind of media. As a consequence, rapid change in the relative popularity of tourist attractions is inevitable. Against this, knowledge of tourist attraction hotspots is essential in tourism management. This means there is a need to study how to both quickly determine the popularity level of tourist attractions and encompass a relatively large area. This article utilised tweet data from microblogging website Twitter as the basis from which to determine the popularity level of a tourist attraction. Data mining was conducted using Python and the Tweepy module. The tweet data were collected at the end of April and early May 2017, at times when there are several long holiday weekends. A Tweet Proximity Index (TPI) was used to calculate both the density and frequency of tweets based on a defined search radius. A Density Index (DI) was also used as a technique for determining the popularity. The results from both approaches were then compared to a random survey about people’s perceptions of tourist attractions in the study area. The result shows that geotagged tweet data can be used to determine the popularity of a tourist attraction, although it still only achieved a medium level of accuracy. The TPI approach used in this study produced an accuracy of 76.47%, while the DI achieved only 58.82%. This medium accuracy does indicate that the two approaches are not yet strong enough to be used for decision-making but should be more than adequate as an initial description. Further, it is necessary to improve the method of indexing and the exploration of other aspects of Twitter data.

Cities have a common characteristic in the form of land utilisation, which is dominated by built-up areas. Tourism is an essential aspect of city development because it can involve the identity of the city. Historical buildings, landmarks, shopping centres and museums are generally interesting places for tourists to visit. Yogyakarta, the research area, is synonymous as a city of culture and of students. Knowledge of the spatial clustering patterns of tourists can be one of the references for urban development. Social media data were used in the study as an alternative to direct data collection, which requires considerable resources. Flickr and Twitter were used as proxies to dete rmine the distribution of tourists, and the DBSCAN and HDBSCAN clustering algorithms were used to determine the centres of tourist activity. Furthermore, Flickr data were analysed temporally to determine the impact of the COVID-19 pandemic on tourism in Yogyakarta City. The clustering of social media data results shows that there are several city hotspots, besides the already well-known tourist attractions. Apart from city landmarks, several other tourist hotspots were revealed through the clustering process, such as accommodation, shopping centres, entertainment venues and souvenir shops, which also support tourism activities. The impact of COVID-19 on tourism in Yogyakarta City can be reflected through the number of uploaded photos by tourists on Flickr, which has decreased since March 2020.

Indonesia has many cultural heritages that attracts not only local tourist but also a foreigner. The most renowned site for cultural tourism is Borobudur, that attracts many tourists. It also included as one of the seven cultural wonders in the world. Tourism activity cannot be separated from photography since the visitors would want to have memories of the locations. Involuntary Geographic Information (iVGI) is one of the new sources of information that can be used to analyze the pattern of human activities spatially. This research explores Flickr data as an example of using photo-based iVGI data for hotspot analysis of human activities in cultural tourism objects. Each photo in Flickr's database located in Borobudur can be assumed as an activity log since Flickr allowed the user to add geotagged photos.

Abstrak Tsunami merupakan bencana alam yang sebagian besar kejadiannya dipicu oleh gempabumi dasar laut. Dampak kerugian tsunami terhadap lingkungan pesisir antara lain rusaknya properti, struktur bangunan, infrastruktur dan dapat mengakibatkan gangguan ekonomi. Bencana tsunami memiliki keunikan dibandingkan bencana lainnya, karena memiliki kemungkinan sangat kecil tetapi dengan ancaman yang tinggi. Paradigma Pengurangan Risiko Bencana (PRB) yang berkembang dalam beberapa tahun terakhir yang menekankan bahwa risiko merupakan hal utama dalam penentuan strategi terhadap bencana. Kelurahan Ploso, merupakan salah satu lokasi di Kabupaten Pacitan yang berpotensi terkena bencana tsunami. Pemetaan risiko bangunan dilakukan dengan metode kuantitatif, yang mana disusun atas peta kerentanan dan peta harga bangunan. Papathoma Tsunami Vulnerability 3 (PTVA-3) diadopsi untuk pemetaan kerentanan. Data harga bangunan diperoleh dari kombinasi kerja lapangan dan analisis Sistem Informasi Geografis (SIG). Hasil pemetaan risiko menunjukkan bahwa Lingkungan Barehan memiliki risiko kerugian paling tinggi diantara semua lingkungan di Kelurahan Ploso. Hasil ini dapat dijadikan sebagai acuan untuk penentuan strategi pengurangan risiko bencana di Kelurahan Ploso. Kata kunci : Risiko bangunan, tsunami, PTVA-3, Pacitan Abstract Tsunami is a natural disaster whose occurrences are mostly triggered by submarine earthquakes. The impact of tsunami on coastal environment includes damages to properties, building structures, and infrastructures as well as economic disruptions. Compared to other disasters, tsunamis are deemed unique because they have a very small occurrence probability but with a very high threat. The paradigm of Disaster Risk Reduction (DRR) that has developed in the last few years stresses risk as the primary factor to determine disaster strategies. Ploso Sub-district, an area in Pacitan Regency

Pemetaan desa menjadi salah satu fondasi untuk melakukan pembangunan desa. Metode kartometrik menjadi cara yang banyak digunakan untuk menarik batas desa yang didukung dengan berbagai macam data geospasial misalnya Citra Satelit Resolusi Tinggi (CSRT) dan Digital Elevation Model (DEM). Peta kerja yang digunakan sebagai data utama dalam diskusi para pemangku kepentingan untuk menarik batas desa, semestinya disusun secara optimal untuk mempermudah penarikan garis batas. Umumnya pengenalan batas desa pada daerah perkotaan terbantu dengan penggunaan CSRT, karena objek yang menjadi penanda batas mudah dikenali. Namun demikian, pada daerah berbukit pengenalan batas desa dari CSRT relatif lebih sulit dilakukan, karena minimnya unsur buatan manusia yang umumnya menjadi penanda batas. Penelitian ini bertujuan untuk melakukan optimalisasi peta kerja dengan memanfaatkan kombinasi geovisualisasi 2D, 2,5D, dan 3D untuk penarikan garis batas desa pada daerah berbukit. Geovisualisasi tersebut didukung dengan menggunakan data DEMNAS untuk menghasilkan hillshade yang disajikan dengan teknik multi hillshade. DEMNAS digunakan karena memiliki resolusi spasial yang cukup tinggi (0.27-arcsecond) dan bersifat open access. Data lain yang digunakan adalah CSRT, peta kontur, dan peta jaringan sungai. Hasil penelitian menunjukkan bahwa dalam penarikan batas desa pada daerah berbukit, diperlukan peta kerja yang mengombinasikan geovisualisasi dari berbagai dimensi. Dalam hal ini visualisasi 2,5D dan 3D dapat membantu pengenalan objek perbukitan seperti punggung dan lembah, sehingga delineasi dapat dilakukan dengan lebih mudah. Informasi tambahan seperti adanya data pilar batas dan ketersediaan sumber daya manusia yang mengerti batas desa akan semakin mempermudah proses penarikan garis batas desa.