These last few years, a lot of tourism village appeared in various regions, as well as in Sleman, Yogyakarta. Department of Tourism and Culture Sleman, noted, until 2015 there were a total of 35 tourism village with a variety of categories. With many of the tourism village, is necessary to manage the tourism village well. Mapping the location of the tourism village in Sleman district, conducted to provide ease of access roads to tourist sites for tourists domestic and foreign tourists. Software used is ArcGIS 10.1. with ArcGIS can make a map of Tourism Village Location Sleman 2015.

A Very high intensity of regional development is ubiquitous in urban areas. Therefore, urban development requires a proper spatial development strategy in many facets, especially social aspect and disaster potential. The essence of social aspect lies in the prevailing norms and local wisdom that have long existed and become the basis of community life. Inducing various effects on infrastructure development, disaster potential has to be considered as well. Disaster mitigation measures can start with the use of continually developing remote sensing technology, which provides a basis for preparing sustainable development planning. The realization of these measures in urban areas demands specific adjustment to the environmental conditions. This study aimed to examine the capacity of remote sensing data to support disaster mitigation and infrastructure planning based on energy conservation in urban areas. The results indicate that remote sensing technology can be an option for sustainable development planning in urban areas.

ABSTRAKSecara umum perilaku pedagang di pasar Karangwaru Yogyakarta dalam menangani sampah mayoritas selalu membuang sampah pada tempatnya saat berada di rumah, di pasar, jalan, dan tempat ibadah. Alasan dari perilaku tersebut karena kebiasaan dan agar hidup sehat. Namun demikian masih terdapat beberapa pedagang yang tidak membuang sampah pada tempatnya. Penyebab utamanya adalah tidak adanya fasilitas tempat sampah. Pengetahuan terkait manfaat sampah dan penyakit yang dapat disebabkan oleh sampah sudah diketahui oleh sebagian besar pedagang. Sedangkan untuk perilaku mencuci tangan, secara umum pedagang selalu mencuci tangan sebelum makan, sesudah makan, setelah dari kamar mandi, ketika merasa kotor, dan setelah bekerja dengan alasan agar hidup sehat. Namun demikian pedagang tidak terbiasa mencuci tangan dengan sabun dikarenakan tidak adanya fasilitas (sabun) yang disediakan di tempat cuci tangan atau toilet.

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Piecewise constant (PC) is a stochastic model that can be applied in various fields such as engineering and ecology. The stochastic model contains a noise. The accuracy of the stochastic model in modeling a signal is influenced by the type of noise. This paper aims to propose inverse-gamma noise in the PC model and the procedure for estimating the model parameters. The model parameters are estimated using the Bayes approach. Model parameters have a variable dimension space so that the Bayesian estimator cannot be determined analytically. Therefore, the Bayesian estimator is calculated using the reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm. The performance of the RJMCMC algorithm is validated using data synthesis. The finding is a new PC model in which the noise has an inverse-gamma distribution. In addition, this paper also proposes a parameter estimation procedure for the model based on an RJMCMC. The simulation study shows that the model parameter estimators generated by this algorithm are close to the model parameter values. This paper concludes that inverse gamma noise can be used as an alternative noise in the PC model. The RJMCMC is categorized as a valid algorithm and can estimate the PC model parameters where the noise has an inverse-gamma distribution. The novelty in this paper is the development of a new stochastic model and the procedure for estimating the model parameters. In application, the findings in this paper have the potential to improve the suitability of the stochastic model to the signal.