Spatial Micro Level Analysis of Building Structures in Samos Island
Decision-making at the regional level has become more complex over the last years, requiring advanced tools to cope with dynamic environments and processes; and a thorough analysis of spatial entities in finer scale for better understanding of the dynamics and underlying processes. The focus of this paper is on the contribution of fine scale datasets in policy making by use of Spatial Micro Level Analysis of building-structures data. This approach enables the exploration of the spatial pattern in finer scale, setting the ground for a better insight in micro level dynamics. The proposed approach was applied in an insular area—Samos, Greece—in an effort to study two defining issues of islands’ territory development in the Aegean Sea nowadays, namely informal settlements’ expansion as well as spatial distribution of fire events, which are closely linked to pressures exerted on such areas by current development patterns as well as climate change impacts. The scope of this work is to illuminate underlying mechanisms of attraction/repulsion of informal housing; and identify the relationship between points of fire ignition and populated areas. Output of such an approach can feed decision-making processes and support more “smart” policy directions for coping with challenges of both island territory development and fire-related risks.
KeywordsGeographical analysis Insular areas Spatial micro level data Informal settlements’ development Risk of fire events
- Al-Hader, M., & Rodzi, A. (2009). The smart city infrastructure development and monitoring. Theoretical and Empirical Researches in Urban Management, 4(2), 87–94.Google Scholar
- Alonso-Betanzos, A., Fontenla-Romero, O., Guijarro-Berdiñas, B., Hernández-Pereira, E., Inmaculada Paz Andrade, M., Jiménez, E., Luis Legido Soto, J., & Carballas, T. (2003). An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Systems with Applications, 25(4), 545–554.Google Scholar
- Bivand, R. S., Pebesma, E. J., & Gómez-Rubio, V. (Eds.). (2008). Applied spatial data analysis with R. New York: Springer.Google Scholar
- Black, K. (2014). R object-oriented programming. Birmingham: Packt Publishing.Google Scholar
- CRAN. (2015). The comprehensive R archive network. http://cran.r-project.org/. Accessed November 3, 2016.
- ELSTAT. (2016). Hellenic Statistical Authority. http://www.statistics.gr/. Accessed November 3, 2016.
- Frank, R. (2000). Understanding smart sensors. Bristol: IOP Publishing.Google Scholar
- Geography of Natural Disasters Lab. (2016). Geography of Natural Disasters Lab. University of the Aegean (Department of Geography). http://catastrophes.geo.aegean.gr/. Accessed July 13, 2016.
- Google (2016). Google Earth. http://earth.google.com/. Accessed November 3, 2016.
- Greek Geodata Portal. (2016). GEODATA.gov.gr. http://geodata.gov.gr/. Accessed July 12, 2016.
- Hellenic Fire Service (2016). Hellenic fire service. http://www.fireservice.gr/pyr/site/home.csp. Accessed July 13, 2016.
- Kavroudakis, D. (2015). Sms : An R package for the construction of microdata for geographical analysis. Journal of Statistical Software, 68(2). doi: 10.18637/jss.v068.i02
- Kavroudakis, D., Ballas, D., & Birkin, M. (2013). SimEducation: A dynamic spatial microsimulation model for understanding educational inequalities. In R. Tanton & K. Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 209–222). Dordrecht: Springer.Google Scholar
- Kavroudakis, D., & Kyriakidis, P. (2013). DTH 1.0: Towards an artificial intelligence decision support system for geographical analysis of health data. European Journal of Geography, 4(3), 38–49.Google Scholar
- Nuaimi, E. A., Neyadi, H. A., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(25), 1–15.Google Scholar
- Pinder, D. A., & Witherick, M. E. (1972). The principles, practice and pitfalls of nearest-neighbour analysis. Geography, 57(4), 277–288.Google Scholar
- PostgreSQL Global Development Group. (2016). PostgreSQL: The world’s most advanced open source database. https://www.postgresql.org/. Accessed November 3, 2016.
- QGIS (2016). QGIS: A free and open source geographic information system. http://www.qgis.org/. Accessed November 3, 2016.
- QGIS Project Team. (2016). A gentle introduction to GIS. http://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/. Accessed November 3, 2016.
- Russom, P. (2011). Big data analytics (report: fourth quarter). TDWI Research. https://www.google.gr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjh2tX9o43QAhVH1hoKHdKSCsYQFggeMAA&url=https%3A%2F%2Ftdwi.org%2Fresearch%2F2011%2F09%2F~%2Fmedia%2FTDWI%2FTDWI%2FResearch%2FBPR%2F2011%2FTDWI_BPReport_Q411_Big_Data_Analytics_Web%2FTDWI_BPReport_Q411_Big%2520Data_ExecSummary.ashx&usg=AFQjCNGXAd3CSMIuiVEp9TtNL97F6aZylA&sig2=RJvLwyBX-tNQR1B4ZTy-2w. Accessed November 3, 2016.Google Scholar