AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
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
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
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”