Sidiropoulos & Stergiou - input2012
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The document discusses gentrification in Athens, focusing on the displacement of lower-income populations by more affluent groups and the implications on urban dynamics. It presents various spatial analysis tools and methodologies to study gentrification effects and includes a case study emphasizing Athens' unique characteristics. The conclusions indicate a lack of standardized strategies for addressing gentrification, highlighting the need for tailored approaches based on local conditions.
Sidiropoulos & Stergiou - input2012
- 1. LOGO Seventh International Conference on Informatics and Urban and Regional Planning Gentrification & Spatial Analysis Tools: The Perspective of Implementation in the City of Athens Sidiropoulos G., Stergiou M. Friday, May 11th
- 2. Contents Introduction The Theoretical Framework Methodology Discussion Conclusion 2
- 3. Introduction Gentrification refers to the displacement of lower income population by the relocation of the middle class at renovated or renewed properties of central city neighborhoods . New geographies are produced Which models approach these areas? The degree of Implementation? 3
- 4. The Theoretical Framework Urban Renewal Gentrification Consumer’s Key Consumerism, Access, Increase of single preferences Reasons parent households, the change in cultural Values & standards... Middle class, highly educated, without The kids, good salary, access. Characteristics Empty buildings, with low rents & significant cultural/historical value. (-) shifting, homelessness, abandonment, Rent Gap Change of the The effects Devaluation, loss of population.. Economic (+) social involvement, relieve poverty, Theory Base Grants, property taxes, preservation.. 4
- 5. Methodology Gentrification Model Urban Models Relations Visualization Indicators* Case study Methods & Tools Theoretical Background 5
- 6. Spatial Analysis Tools The best Spatial analysis tools for Gentrification GIS CA 1. Visualization & 1. Visualization & Analysis, Re- Analysis Visualization 2. Complex Urban 2. Degree of Phenomena Interaction 3. Dynamic 3. Flexible 4. Only for theory of according to the Rent Gap data 5. Uncertainty of the 4. Precision in the results results 6. Micro-scale data 6
- 7. Spatial Analysis Tools ΙCTs Cellular GIS Automata Indicator of Gentrification ! (Source: Mantelas, p.8) 7
- 8. Cellular Automata Typical diagram of cellular automata in a period of 60 years (Source: O’ Sullivan., p.269) Recording of a random pattern in unit time. (Source: Batty., p.18) 8
- 9. The Specificity of Athens Small Point scale Social Housing crisis stock Methods Data & Tools 9
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- 12. Gazi Area Gazi Area Gazi Area Metaksourgeio Metaksourgeio Metaksourgeio 12
- 13. Notes • Population // N. Buildings/housing block • Empty buildings… • Low population density • Low housing density • Close to city center • Close to historical areas • Access public/private transport • High Objective Values wherever there are Banks • Access • Close to city center • Appearance of the phenomenon only in a few neighborhoods 13
- 14. Discussion 1 2 3 -Standardization of -Detailed data -Dynamic model, the concept of collection -Detection process & Gentrification and -Record changes control. testing the process -Explanation of the -Further research in of implementation. diverse aspects of Greek -Specification of Gentrification in neighborhoods. key parameters, Athens. assess & evaluation. 14
- 15. Conclusions There is no standard strategy for the 1 implementation Specificity of Athens as far as software, 2 data and the proper model 3 Not the same degree in Implementation 4 There are prospects 15
- 16. References Alexandri G. (2011). The Breeder Feeder: Tracing Gentrification in Athens City Center, The struggle to belong, Dealing with diversity in 21sr century urban settings. Amsterdam. Batty, M. (2007). Cities and Complexity. Massachusetts: The MIT Press. Clarke K. , G. L. (1998). loose-coupling a cellular automata model and GIS: long term urban growth prediction for San Francisco and Washington/Baltimore. Geographical Information Science, 12(7), 699- 714. Cliff A., H. P. (1996). The Impact of GIS on epidemiological mapping and modelling. In B. M. Lougley P. (Ed.), Spatial Analysis: Modelling in a GIS Environment. Canada: John Wiley & Sous.INC. Diappi, L., & Bolchi, P. (2008). Smith's rent gap theory and local real estate dynamics: A multi-agent model. Computers, Environment and Urban Systems, 32(1), 6-18. Dritsa A. (2009), Areas with Urban Renewal -phenomena of Gentrification- the example of Metaksourgio, National Technical University of Athens. O'Sullivan, D. (2002). Toward micro-scale spatial modeling of gentrification. Journal of Geographical Systems, 4(3), 251-274. Roy G., F. S., G. Zaitseva (2000). Spatial Models and GIS. In F. S. Wegenen M. (Ed.), Spatial Models and GIS (pp. 185-201). London: Taylor and Francis. Samat, N. (2007). Integrating GIS and Cellular Automata Spatial Model in evaluating urban growth: prospects and challenges. Jurnal Alam Bina, 9(1), 79-93. Soheil Sabri, A. Y. (2008a). Exploring urban modelling methodologies to better figure out urban gentrification dynamics in developng countries. Jurnal Alam Bina, 11(2), 29-43. Sidiropoulos G. & Stergiou M. (2010). Gentrification and Spatial Analysis Tools (CA/GIS), HellasGI, 6th Conference, National Technical University of Athens, Athens. Takala, A. (2006). Evaluating urban regeneration - How to measure relevance of a new urban structure? , University of Tampere, Tampere, Finland. Takeyama, M., & Couclelis, H. (1997). Map dynamics: integrating cellular automata and GIS through Geo- Algebra. International Journal of Geographical Information Science, 11, 73-91. 16
- 17. LOGO Thank you! “Designing a dream city is easy; rebuilding a living one takes imagination”