In the world today and in Turkey, poverty and its alleviation has become an important issue. As a result, detailed studies for the identification of poverty need to be done. In the recent years, the spatial aspect of the multidimensional character of poverty is gaining significance. For this purpose, in this thesis, spatial aspects of poverty are tried to be analysed using Geographic Information Systems (GIS) in the case of Ke&ccedil / i&ouml / ren District in Ankara. Firstly, a digital map of the spatial distribution of the urban poor living in Ke&ccedil / i&ouml / ren is made and linked to the database to analyse the spatial distribution. The poverty database used in this study is based on the data collected by the Social Assistance and Solidarity Foundation in the district. It includes state of poverty and some sociodemographic characteristics of the households who applied for social assistance. The analyses with respect to the methodology of this study aims at finding the common characteristics of poor settlements and the areas/households which are the poorest of the poor in Ke&ccedil / i&ouml / ren. The maps obtained as a result of spatial data analysis indicate the dense living areas of the poor, clusters of poor households, neighbourhood level poverty analysis and poor areas within neighbourhoods. There are also additional analyses which compare the characteristics of the geographical distribution of the poor with other aspects, such as land values, roads and building conditions. Such a study can be helpful to re-allocate the poverty alleviation efforts more efficiently by determining priority areas.

Natural hazards are the reality of today&amp / #8217 / s world, which considerably affect people&amp / #8217 / s living conditions. As they cannot be prevented, the basic precautions should be taken before the occurrence to protect people. At this point, the preparedness for any threat is really important, which does decrease destructive effects of the hazard for communities and shorten recovery interventions. In terms of preparedness, identification of vulnerable people in the community gives an important contribution for better planning in disaster management. In this respect, this thesis aims to develop a methodology in order to define vulnerable groups in terms of their social conditions for any possible hazard, with Geographic Information Systems (GIS) technology. Moreover, the thesis aims to find out an interrelation between hazards and vulnerability, to build awareness about identification of socially vulnerable groups in the pre- and post-disaster planning. A case study area is selected in earthquake-prone Pendik, Istanbul, in order to find the contribution of the assessment. A study is carried out to describe social vulnerability levels in the study area using GIS. Criterion standardization, weighting and combining are accomplished by multi criteria evaluation methods. These calculations are supported with five explorative spatial data analyses to understand global trends and spatial interactions of the study data. The objectivity of the assessment and the complicated structure of the study data are also discussed. The main outcomes of the methodology and its applications in the case study area show that, the southeast part of Pendik is socially vulnerable to any possible hazard.

Census operations are very important events in the history of a nation. These operations cover every bit of land and property of the country and its citizens. Census data is also known as demographic data providing valuable information to various users, particularly planners to know the trends in the key areas. Since 2006, Turkey aims to produce this census data not as &ldquo / de-facto&rdquo / (static) but as &ldquo / de-jure&rdquo / (real-time) by the new Address Based Register Information System (ABPRS). Besides, by this new register based census, personal information is matched with their address information and censuses gained a spatial dimension. Data obtained from this kind of a system can be a great input for the creation of &ldquo / small statistical areas (SSAs)&rdquo / which can compose of street blocks or any other small geographical unit to which social data can be referenced and to establish a complete census geography for Turkey. Because, statistics on large administrative units are only necessary for policy design only at an extremely abstracted level of analysis which is far from &quot / real&quot / problems as experienced by individuals. In this thesis, it is aimed to employ some spatial clustering and districting methodologies to automatically produce SSAs which are basically built upon the ABPRS data that is geo-referenced with the aid of geographical information systems (GIS) and thus help improving the census geography concept which is limited with only higher level administrative boundaries in Turkey. In order to have a clear idea of what strategy to choose for its realization, small area identification criteria and methodologies are searched by looking into the United Nations&rsquo / recommendations and by taking some national and international applications into consideration. In addition, spatial clustering methods are examined for obtaining SSAs which fulfills these criteria in an automated fashion. Simulated annealing on k-means clustering, only k-means clustering and simulated annealing on k-means clustering of Self-Organizing Map (SOM) unified distances are deemed as suitable methods. Then these methods are implemented on parcel and block datasets having either raw data or socio-economic status (SES) indices in nine neighborhoods of Ke&ccedil / i&ouml / ren whose graphical and non-graphical raw data are manipulated, geo-referenced and combined in common basemaps. Consequently, simulated annealing refinement on k-means clustering of SOM u-distances is selected as the optimum method for constructing SSAs for all datasets after making a comparative quality assessment study which allows us to see how much each method obeyed the basic criteria of small area identification while creating SSA layers.