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This research is based on the facts: first that, Boyolali is one of the regions which implement intensively many kinds of program in solving the poverty which gets the finance from APBD, central government and international institutions, eventhough the proportion of the poor society increases significantly.The proportion of poor society increases 20,8% in 2002 becomes 38,26% in 2006. Second, seen from the regional development indicator, it is shown that between one region and the others has various levels of the varieties of development.The objectives of this research are: first, the understanding of the distribution and of the poverty level in this region. Second, the understanding of the relationship between distribution of poverty level and the regional development level. Third, the understanding of the factors which influence the regional development. The method used in this research is secondary data analysis. The analysis unit of this research is village. The data resources are taken from the report of the identification result of poor families and the primary data is taken from BAPPEDA Boyolali. The primary data is a number of poor families, the regional scope and the use of farmland, the long street to account the regional accessibilities and the number and the distribution of social and economical facility in each village. The result is presented on the map with the analysis unit of the village. The represented map are the distribution level of poverty per village. To determine the relationship between the level of poverty and regional development uses the technique of qualitative and quantitative analysis. The qualitative analysis technique used is the analysis of the map of poverty distribution, analysis map of regional development and harmonious relationship between the level of regional development and poverty. The quantitative analysis technique used is the analysis of correlation statistic product moment.The results of this research are: first, there is distribution variation of poverty level, there is relationship between distribution of poverty level and natural resources endowment.The region with lower resources endowment (up land region) have higher poverty level than the region with higher natural resources endowment (law land region) and conversel. Second, there is negative relationship between regional development level and poverty level.Third, the factors which influence the level of regional development are the economical and social facility of the region and accessibilities.

Nowadays, Urban Heat Island (UHI) occurs in big cities worldwide. The UHI phenomenon needs to be introduced in school because this phenomenon occurs around the students. Surakarta, one of the big cities in Indonesia, has been threatened by the UHI phenomenon, so enrichment materials related to the UHI phenomenon are needed for students in schools. This research will develop the UHI e-module as an enrichment teaching material on the impact of global climate change and research on climate and its utilization. This paper aims to present an e-module development research methodology on UHI based on the phenomenon of UHI threats in Surakarta City and its effect on student achievement and collaboration skills. Design and Development Research (DDR) uses the Borg and Gall model. The methodology of the research development is divided into three phases: the needs analysis phase, the design and development phase, and the implementation and evaluation phases. The difference in this research is the geographical space-based study approach in the development of material based on the UHI phenomenon in Surakarta City and urban and rural spatial sampling techniques.

Earthquakes are disasters that often occur in Indonesia. Many students in Indonesia became victims when the earthquake occurred. Therefore, the behavioral response of students when facing disasters is very important to understand. This behavioral response will not manifest spontaneously, it takes time and considerable effort to realize a good behavioral response to disasters. One ability that is currently being developed in students is disaster literacy, a good disaster literacy can help students to know the right attitude when a disaster occurs. This study aims to determine the effect of disaster literacy on student behavioral responses in an effort to reduce earthquake risk at SMA Negeri 1 Klaten. There are 2 variables, namely the independent variable with 4 indicators that is basic disaster literacy, functional disaster literacy, communicative / interactive disaster literacy and critical disaster literacy, while the dependent variable has 4 indicators, namely preparedness, awareness, action and affect. This study uses a quantitative method with a correlational design. Respondents in this study amounted to 269 students, and data collection techniques were carried out using questionnaires, interviews and documentation. The data analysis technique used the Pearson Bivariate Correlation Test. The results showed that there was a significant relationship (sig. 0.672) between disaster literacy and student behavior responses in an effort to reduce earthquake risk at SMA Negeri 1 Klaten. The existence of this significant relationship should be utilized by every school that located in disaster-prone areas to develop several programs that focus on disaster literacy for students.

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.