Spatial and temporal distribution of estimated surface runoff caused by land use/land cover changes in the upstream Citarum watershed, West Java, Indonesia

In Indonesia, flooding is one of the natural hazards that often occurs during the rainy season. Surface runoff coefficient values are an essential indicator of the supply of regional water resources. The smaller the surface runoff value, the greater the water storage in the ground, and the smaller surface was running water. This study analyses the spatial and temporal distribution of the estimated surface runoff caused by land use/land cover changes in the upstream Citarum watershed. The study area is located in the upstream Citarum watershed, West Java, Indonesia. The site has a long history of flooding and various complex environmental problems. The geographic Information System method was used as a tool in analyzing the spatially and temporally. The research result shows that there has been a change in land cover in several periods of the year in the Citarum upstream watershed. The occurrence of the LULC phenomenon positively affects the surface runoff coefficient. The increasing area of Built land and plantation in the Citarum upstream watershed will further increase the surface runoff coefficient and, in the end, will potentially increase the surface runoff and contribute to flooding in the Bandung basin. This study results can be used to provide input in determining the direction and policies for watershed management, taking into account the varying characteristics of each subwatershed.

Mining Flickr to Better Understand Tourist Behavior

The aim of this chapter is to present an automated instrument collecting the enormous amount of information available online allowing urban planners, public administrations, tourism services suppliers, and researchers to easily understand the spatial and temporal distribution of tourist behaviors towards tourist attractions in a specific area. Geo-located photos provided by Flickr are used to identify points of interest (POIs). The developed application has been tested with data automatically retrieved and collected in L'Aquila province (Italy) during the years 2005-2018. Given the richness of information, these data are able to show how POIs changed over time and how tourists reacted to the 2009 earthquake. Results demonstrate the importance of using analytics and big data in tourism research. Moreover, by using the province of L'Aquila as pilot study, it emerges that tourist behaviors change over time and space, varying among different typologies of tourists: residents, domestic, and international visitors.