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Gentrification & Spatial Analysis Tools:

The Perspective of Implementation in the City of Athens

Seventh International Conference onSeventh International Conference on

Informatics and Urban and Regional PlanningInformatics and Urban and Regional Planning

Sidiropoulos G., Stergiou M.

Friday, May 11th

Contents

Introduction

The Theoretical Framework

2

Methodology

Discussion

Conclusion

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!

3

� New geographies are produced!

� Which models approach these areas?

� The degree of Implementation?

The Theoretical Framework

Consumerism, Access, Increase of single

parent households, the change in cultural

Values & standards...

Middle class, highly educated, without

kids, good salary, access.

Empty buildings, with low rents &

Key

Reasons

The

Characteristics

Consumer’s

preferences

Urban Renewal� Gentrification

4

Empty buildings, with low rents &

significant cultural/historical value.(-) shifting, homelessness, abandonment,

Devaluation, loss of population..

(+) social involvement, relieve poverty,

Grants, property taxes, preservation..

Characteristics

The effectsRent Gap

Theory

Change of the

Economic

Base

Methodology

Gentrification ModelUrban Models

5

Relations

Indicators*

Methods& Tools

Visualization

Case study

Theoretical Background

Spatial Analysis Tools

GIS

The best

Spatial analysis tools

for Gentrification

CA

6

GIS1. Visualization &

Analysis, Re-

Visualization

2. Degree of

Interaction

3. Flexible

according to the

data

4. Precision in the

results

CA1. Visualization &

Analysis

2. Complex Urban

Phenomena

3. Dynamic

4. Only for theory of

Rent Gap

5. Uncertainty of the

results

6. Micro-scale data

Spatial Analysis Tools

ΙCTs

7

GISCellular

Automata

Indicator of Gentrification ! (Source: Mantelas, p.8)

Cellular Automata

Typical diagram of

cellular automata in

a period of 60 years

8

(Source: Batty., p.18)

(Source: O’ Sullivan., p.269)

Recording of a

random pattern in

unit time.

The Specificity of Athens

PointSmall scale

Social HousingSocial

crisis

Housing

stock

DataMethods & Tools

9

10

11

Gazi Area Gazi Area Gazi Area

Metaksourgeio Metaksourgeio Metaksourgeio

12

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

Discussion

1

-Standardization ofthe concept ofGentrification andtesting the process

2

-Detailed data

collection

-Record changes

-Explanation of the

3

-Dynamic model,

-Detection process &

control.

-Further research in

14

testing the processof implementation.-Specification ofkey parameters,assess &evaluation.

-Explanation of the

diverse aspects of

Gentrification in

Athens.

-Further research in

Greek

neighborhoods.

Conclusions

There is no standard strategy for the

implementation1

Specificity of Athens as far as software,

data and the proper model2

15

Not the same degree in Implementation3

There are prospects,4

References

� Alexandri G. (2011). The Breeder Feeder: Tracing Gentrification in Athens City Center, The struggle tobelong, 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-agentmodel. 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.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.

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