The COVID-19 pandemic has had a huge impact on Indonesia, as many other nations throughout the globe, particularly on the travel and tourism industry. The most noticeable effect is the decline in tourist visitation, which fell by over 75% in 2020 compared to the prior year. Businesses and workers in the tourism industry have been significantly impacted by the fall in visitor numbers, particularly in Yogyakarta, one of the most well-liked tourist sites in the nation. This study intends to investigate the geographical effects of the COVID-19 pandemic on tourism-related activities. A strategy to determine changes in travel behaviour before and during the COVID-19 pandemic uses social media data, such as Flickr and Twitter. Both social media has been extensively used in tourism related studies in the past. Because December is the busiest month for tourism, Twitter data from that month was chosen as the sample. The selected sample ranges are for 2019, 2020, and 2021. While Flickr data covers from 2018 to 2023, to generate a different perspective than that of Twitter data. The study's findings demonstrate how limitations on community activities significantly influence the traditionally popular tourist attractions. Public spaces, dining establishments, and even hotels are preferred travel destinations by tourists.
KEYWORDS: Modeling, Data modeling, Visualization, Web 2.0 technologies, Statistical analysis, Java, Geography, Analytical research, Data conversion, Internet
Nowadays, Twitter data is significant to many studies since there is a shift in the data collection paradigm. As one of the contemporary social media with many active users, Twitter provides geotagging facilities to create a geotagged Tweet. Various spatial based studies use geotagged Tweet data. This paper aims to review the geo-temporal characteristics of geotagged Twitter data in nine major cities in Indonesia, namely five cities in the Greater Area of Jakarta, Surabaya, Bandung, Medan, and Makassar. Twitter data was collected by the streaming method for two years (January 2019- December 2020). The temporal analysis was carried out by graphing the number of Tweets with 30-minute intervals. Weekly Twitter activities were also visualized to get a specific understanding of when the optimum time to post a Tweet was. Density analysis was employed to Twitter data to find out the spatial patterns in the study area. Kernel Density Estimation (KDE) was used to determine the Tweets Density in the day and night. This study also used a simple framework of text analysis of topic modelling using Latent Semantic Indexing (LSI) to use the Twitter data better. Overall, Central Jakarta and South Jakarta have a significant number of Tweets compared to other cities. The study results show that, in general, big cities in Indonesia have almost the same temporal curve and the peak time for making geotagged tweets occurs from 4 pm to 8 pm. Our finding also points out that a high number of the population in a city does not always produce a high number of Tweets. The results of topic modelling in the Greater Area of Jakarta show that the themes of traffic jams/congestion, entertainment, and culinary tourism are widely mentioned by Twitter users, thus opening opportunities for research on these subjects.
KEYWORDS: Landslide (networking), Data modeling, Raster graphics, Java, Roads, Data conversion, Digital imaging, Data processing, Agriculture, Analytical research
Landslide is caused by meteorological and geomorphological factors. Landslide is one of the most common disaster that occur in Indonesia. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 64 landslides in Purworejo. To reduce the impact of landslide, effective evacuation routes are needed. Determining of evacuation routes can be done in various methods, one of methods is use a spatio-cost approach. The purpose of this research is to determine the most effective evacuation routes to reduce the impact of landslide. Spatio-cost parameters obtained by certain paramaters. The parameters are physical parameters and some social parameters derived from the appearance on the surface of the earth, such as housing, number of population, land use, slope direction, roads and also the wide of the roads. These parameters are processed to look for evacuation routes using Least Cost Path (LCP) method. The expected result of this research is evacuation routes that can help people around disaster-prone areas to prepare. This on going research is important to improve disaster manajemen in Indonesia, especially for landslide in Bruno, Purworejo, Central Java.
KEYWORDS: Landslide (networking), Data modeling, Visual process modeling, Visualization, 3D modeling, 3D visualizations, Information visualization, Roads, Raster graphics, Associative arrays
Indonesia is one of the disaster-prone countries. Based on the Indonesian Disaster Information Data (DIBI) and the National Disaster Management Agency (BNPB) in the last five years from 2014 to April 2019 there have been 65 landslides in Purworejo. Landslide is one of the most common disaster that occur in Indonesia. Landslide is caused by meteorological and geomorphological factors. Purworejo is one of the potential area that could be experiencing landslides, because the geomorphological conditions which are included in Menoreh Hills are geographically sloping to very steep. Landslide susceptibility modeling in Purworejo Regency was carried out using three different methods, namely Information Value Model (IVM), Information Value Model-Analytical Hierarchy Process (IVM-AHP) and Information Value Model-Gray Clustering (IVM-GC). Each modeling is conducted using the Natural Breaks (Jenks) method to produce five classes, namely very low, low, medium, high and very high class based on the IVM value of each method. This research’s goal is to visualized 3 maps of modelling results. The visualization used is 3-dimensional mapping. This mapping is intended to make it easier to compare the map results of modeling that have been done before. The expected results of this study are accurate and reliable 3-dimensional visualization to study the advantages and disadvantages of each of the modeling methods used.
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. Though a data cleaning process must be done to filter irrelevant data. Point Density was employed in this study to explore photo distribution in the study area. Data density will act as an indicator that an area is more frequently visited by visitors. Besides, Zonation of Borobudur Region data was used to compare the density and the zone designated by the official document. The results of the study show that the peak of photography activity occurs at 6 am and area arround the Stupa has attracted visitor in undertaking photo shoot activity.
Data related to socio-economic activities in Indonesia mostly used statistical data. Statistics for large numbers of socioeconomics will make it difficult to interpret and analyze because it consists of many columns and rows with each value. Geo-visualization is a visualization of data represented in a geographic coordinate system. Socio-economic statistics can be visualized to facilitate the process of spatial analysis data that considers spatial surface of earth. Study area is in Special Region of Yogyakarta. This study aims to (1) Select, test and find out color symbol scheme most effective classification method for choropleth mapping of Demographic Map, (2) Mapping happiness profile of population using small area estimation method, (3) Analyzing tourist trends based on Instagram data using space time cube visualization. Secondary data used are population and happiness, while primary data uses social media data for tourist visualization. Geo-visualization of population and happiness used choropleth method. In social media geo-visualization for tourists using space time cube geo-visualization with hexagonal tessellation cells. The results obtained are population maps with best classification scheme, happiness maps at different scale levels, and tourist map using space time cube in Yogyakarta Special Region.
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