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DIY's coastal area is one of DIY's development priorities by establishing the site as an economic corridor that impacts physical development, such as settlements in the coastal area of DIY. This study aims to analyze the distribution and density of settlements. The method used in research is a quantitative descriptive and Geography Information System approach. The analysis used in this study is Nearest Neighbour Analysis (NNA) and Kernel Density Analysis with an analysis tool in ArcGIS 10.5. The results of the NNA show that the three districts located on the coast of DIY have the same distribution pattern characteristics, namely, the clustered pattern, which means the NNA index value is <1 or the T value ranges from 0-0.80. The results of the kernel density analysis show that the most significant density is found in Kulon Progo Regency, precisely in Wates District, and Bantul Regency, precisely in Srandakan District. At the same time, for Gunung Kidul Regency, it is less significant, and the density is only centered on one density point. Several factors cause settlement density, including physical factors (topography, slope, soil type, and clean water sources), accessibility (proximity to transportation routes and proximity to the city center), availability of facilities and infrastructure (electricity network, educational facilities, and health), and environmental factors (natural and human resources).Keywords: Settlements, Coastal, NNA, Kernel Density

This research is motivated from the emerging of Smart City (SC) concept which lately has been implemented in Indonesian’s cities such as Jakarta, Bandung, and Surabaya as a mean to tackle city’s problems digitally. The use of Information and Communication Technology (ICT) becomes one of the SC characteristic which transformed into applications and websites. The main purpose of the research is to map the categorization of SC dimension based on the abundance of aplications and web sites for cities management. The explorative and qualitative approach was used as research method involving secondary data collection, categorization scheme, and pairing to Giffinger’s SC dimensions. About 338 SC’s innovation accounted from cities in Indonesia but only 109 registered as applications or websites. From those numbers, we then categorize into 14 typologies of applications/websites based on its methods in tackling urban problems. They are job related information, transport and traffic, education, citizen participation, environmental management, governance, bureaucracy/permission, staffing, health, energy management, disaster mitigation, criminality, entrepreneurship, and culture and tourism. As result, those categorizations are, in fact, more comprehensive than those of Giffinger’s six smart city dimensions by adding two more dimensions namely Smart Energy Management and Smart Disaster Mitigation. Despite the availability of the applications and websites related to them, the implementation is still limited.