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.
Cultural landscapes reflect humanity's creative genius, social evolution, imagination, and spiritual life. The city of Yogyakarta in Indonesia is an ideal example of a cultural landscape reflecting the works of Hindu-Buddhist, Islamic, colonial, reform to contemporary civilization. Reconstructing historical landscapes and regions is critical for preserving historical memory. Geographical names are a possible way to build community identities. Our research aims to trace the multitemporal landscape from historical maps in Yogyakarta. This study conducted a comparative cartographic analysis of several historical maps of Yogyakarta City, focusing on some of the critical changes and phases during the era. We used topographic maps produced by the Topografische Dienst of the Dutch East Indies, the US Army Map Services, and the Indonesian Geospatial Information Agency to trace the historical landscape in Yogyakarta City. We digitized geographical names as they were presented on the historical maps. Indonesia Geographical Features Cataloging was followed to create a geodatabase. The results of this study showed how the dynamics of geographical names change based on historical map tracing. Additionally, there have been changes in the territory boundaries. This dataset of historical geographical names can serve as a database for preserving cultural heritage and as a basis for sustainable development in Yogyakarta City.
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.
Agricultural land conversion is considered the most important type of land use change, particularly in the countries where agriculture is the primary source of income, including Indonesia. The government of Republik Indonesia has issued a number of programs for preserving agricultural land and controlling the conversion rate. One of the programs is known as sustainable agricultural/cropland. To support the program, any information regarding agricultural land conversion is indispensable. The objective of the study is to provide spatial information called the sensitivity of agricultural land. The study was conducted in the urban area of Yogyakarta. As urban growth or expansion mainly changes land use, agricultural land in this area is highly vulnerable to conversion. The sensitivity of agricultural land was analyzed and mapped using a combination of spatial and statistical analysis. Multitemporal land use maps ,i.e., 2000 and 2020 previously derived from remotely sensed imagery, were used as the primary data for the analysis. Logistic regression was used to analyze the conversion probability and determine the sensitivity index. This study shows that about 650,1 hectares of agricultural land had been converted between 2000 and 2020. It left around 1364,55 hectares of agricultural land in 2020. The remaining agricultural lands were classified into three categories regarding their sensitivity, i.e., high, moderate and low. The proportion of area for each category is 7.5%, 42.7% and 49.8%, respectively. Regarding agricultural land preservation, authorized agencies could utilize this information to determine the preservation priority.
The Gunungsewu area is one of karstic regions in the southern part of the island of Java whose a variety of archaeological remains. Archaeological data were scattered around the Gunungsewu region starting from remains of humans fossil and animals, bone artifacts, clamshell artifacts, Pacitanian cultural stone artifacts, and prehistoric caves that show evidence of occupation caves as well as sustain of prehistoric human communities. This research used the model MaxEnt as a method for estimating prehistoric occupation cave sites in the karst area of Gunung Sewu, Gunung Kidul. The objectives of this research were: (1) assessed the ability of DEM Alos Palsar, Sentinel-2a images and GIS data to extract environmental parameters related to prehistoric occupation cave sites. (2) prepared a spatial model for estimating prehistoric occupation cave sites using DEM Alos Palsar image, Sentinel-2a imagery and GIS data for input model MaxEnt (maximum entropy). (3) test accuracy of model MaxEnt to estimated the location of prehistoric occupation caves. This research used 68 location cave as attendance data input in the model MaxEnt. Environmental variables extracted from the 12.5-meter resolution DEM Alos Palsar, Sentinel- 2A images with 10 meters resolution, and GIS data. There were 8 environmental variables used in this study, there are: OBIA valley-hill classification map, distance map of valley base, elevation map, slope map, aspect map, distance map of lineament, lineament density map and map distance from water sources. Modeling using location data input as many as 68 prehistoric occupations pointed caves with 8 environmental variables resulted in modeling performance with an AUC value of 0. 715 with good performance. Modeling produces the results of the jackknife test, analyzes the response curve of the environment variable and probability map in the researched area. Based on the probability map produced, this studied obtained prehistoric cave location data. Therefore, this modeling shows that MaxEnt could be used as a method for estimating archeological sites.
Studies of land use change have been increasingly carried out in recent years. Land use change is seen as one of the fundamental factor for the operation of environmental system from local to global scale. There are various methods to study land use change and cellular automata(CA)-based spatial simulation is a popular one. While this method (CA-based spatial simulation) is widely used in study of land use change, there are many aspects that need to be explored regarding its performance. Exploring the effect of spatial resolution on CA-based spatial simulation of land use change is the main objective of this research. Yogyakarta urban area was preferred as research area because of its interesting characteristic. Built up land is continuously increasing while agriculture land tend to decrease. Yogyakarta urban area consisted of the city of Yogyakarta and its suburban areas. Spatial simulation combined with experimental analysis were used as the main methods. CA-based spatial simulation were performed on different scenarios i.e. different spatial resolution of the data input. This study used three different spatial resolution that are 10 m, 50 m, and 75 m. Univariate statistical analysis against empirical data of land-use change was conducted to determine those spatial resolutions. Performance of CA-based spatial simulation was accessed using Kappa Index of Agreement (KIA) and two indices of spatial pattern, i.e. variance to mean ratio (VMR) and Moran’s I. This study shows that higher spatial resolution of data input tend to generate a more clustered spatial pattern on the simulated map. The minimum and average value of actual land use change area could be utilized as consideration for determining appropriate spatial resolution. Medium spatial resolution particularly for extended spatial simulation produce more “visually realistic” spatial pattern.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.