City of Jerez Urban Core Densification proposal; An Agent Based Spatio-temporal model

Transcription

1 Modelling urban population allocation City of Jerez Urban Core Densification proposal; An Agent Based Spatio-temporal model Elke Sauter Julia Úbeda

2 Who we are & why we are here Masters programme: Geographical Information Management and Applications (GIMA) Joint initiative between four Dutch Universities Broader context: Msc project as a pilot of a PhD Advanced planification model, new ICT and their applicability in urbanism: Spatial information, scenario simulation and impact evaluation - Irene Luque Irene Luque

3 3 Content Problem Definition Why to model? Question to be answered Agent-based modeling Modeling exercise: conceptualization and implementation Results, conclusions and discussion

4 Problem Definition

5 Jerez de la Frontera 5

6 Demographics: National Statistical Institute INE (Instituto Nacional de Estadística) 6

7 Where to accommodate this incoming population? Jerez Municipality drafted an Expansion Plan Aiming to control the population allocation 7

8 What does expansion actually mean for a city like Jerez? Huge investment in new infrastructure higher taxes to pay by its citizens Loss of the sense of community + reduce liveability of the inner city Degradation of valuable ecosystems and economic drivers in the region Source: byyourbesttraveler.com 8

9 Alternative Local Urban Planners, on behalf of citizens, support the densifying scenario taking advantage of the vacant space existing in the inner city 9

10 Why to model?

11 Why to model? What can be done? Real necessity of making the solution visual & understandable 11

12 Why to model? What can be done? Leads to Informed Stakeholders Better and supported decisions Municipality Jerez citizens Municipality 12

13 Research Question

14 Question to be answered: Expand or densify? To what extent can the Jerez inner city accommodate the upcoming population? AND When should the city expansion plan be implemented to guarantee an optimal functioning of the existing city? Population allocation WHERE? Timing WHEN? Circumstances HOW? 14

15 Agent Based Model

16 What is an agent? 1 single autonomous entity expresses behavior by making decisions by taking actions to meet their objectives -Agarwal et al., 2002; Joffre et al., 2015

17 Agent-Based Modelling A computational tool to simulate: Complex systems in non-computing related scientific domains: biology, ecology & social science Dynamically interacting components Emergence - can give rise to collective behavior -New England Complex Systems Institute (n.d.)

18 AB models for urban planning in the frame of the PhD Complex systems: B A C Behavior Decision making processes -Joffre et al. (2015) Heterogeneous stakeholders diverse opinions varying viewpoints -Salvini et al. (2016) Contexts scenarios

19 Gama Uses the GAML agent-oriented programming language Connects GIS-based data to populate the model Enables agentification capabilities for geo-data Census Units (CU)= agent CU=neighborhood Agent Geo-data

20 Modelling exercise

21 Steps 1. Setting goals 2. Urban indicators Conceptualization 3. Implementation 4. Validate 5. Talk to lawmakers

22 Urban Indicators of the Built Environment An indicator quantifies and aggregates data that can be measured and monitored to determine whether change is taking place (FAO, 2002) Land Use Mobility Urban Indicators Infrastructure Population

23 Attractiveness CU attractiveness as a force to pull people in Level of data aggregation CU#1 to evaluate when CU Attractiveness scores urban expansion plan should be put into effect

24 Attractiveness Scores Population Score Range [1-5] Land Use Score Range [1-5] Combined CU Attractiveness Score Infrastructure Score Range [1-5] Mobility Score Range [1-5]

25 Score Attractiveness Scores: Population 6 % Pop. Score % Population

26 Attractiveness Scores: Land Use Land Use Score MIX 5 RES, PF, EA 3 Dynamic use of space by residents

27 Attractiveness Scores: Mobility Attractiveness Scores: Infrastructure Not implemented!

28 Agent-Based Model (Jerez Overview) Topology inside Jerez City Boundary Environment Part of Is a Agent CU Censal Unit (CU) Part of Attributes Part of Part of Land Use Population Part of Mobility Infrastructure

29 State Variables Initial Data - CU level User Input Capacity Level Population Vacant Space Capacity of People Living Area Built Residential Surface Maximum Population To calculate pop. score Calculations

30 Living Area Living Area Japan 18 m 2 Spain 35 m 2 United States 66 m 2 -Jayantha & Hui, n.d.

31 Capacity Level Capacity Level Measure of how much something can hold 10% Free flow cars Infrastructure (pipelines) empty Lonely feel 50% 100% Traffic jams Infrastructure (pipelines) max/ full Crowded & busy Urban planners can influence city operational levels!

32 Allocation Process Overview 1. Incoming Population **year= year+1 **if necessary Year Pop Check If CU max attractiveness score not reached full capacity # people allocated < incoming population 5. Update CU capacity Iteration t=n year= Select CU + Allocate CU# 17 Score: Recalculate attractiveness scores CU# 5 Score: 20

33 Results

34 Population Allocation

35 Results Table Higher capacity + lower living area = densification Japan 18 m 2 Spain 35 m 2 United States 66 m 2 Capacity Level Year Expand Pop. Leftover Capacity Level Year Expand Pop. Leftover Capacity Level Year Expand Pop. Leftover 70% 201X XX,XXX 80% 202X X,XXX 90% 202X X,XXX 100% 202X X 70% ,363 80% ,551 90% , % ,890 70% 201X XX,XXX 80% 202X X,XXX 90% 202X X,XXX 100% 202X X

36 Spain- Jerez -1 Original State Year

37 Spain- Jerez -2 Allocation: 70% Capacity Level Reached Expand by year

38 Spain- Jerez -3 Allocation: 100% Capacity Level Reached Expand by year

39 Conclusions & Discussion

40 Conclusions & Reflections Living area rule and capacity levels have high impacts on the model. Capacity levels vs. attractiveness of CU s: find equilibrium between the two be aware of the risks you want to assume determinant of when to expand Urban planners/ Municipality of Jerez to evaluate whether densification is a good policy to deal with the incoming population. Agent based model as a tool for modelling urban complex systems.

41 Discussion and future work Inclusion of other urban indicators for more holistic approach: mobility and infrastructure housing prices demographic characterization of agents and their preferences Evaluating sustainability (economic, environmental and social impacts) Methodology can be replicated with case specific parameters

42 Questions?

43 Bibliography Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Charles M. Schweik. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice, Retrieved from Draft_9.pdf Food and Agriculture Organization of the United Nations (2002) Pressure-State-Response Framework and Environmental Indicators. Available from: programmes/en/lead/toolbox/refer/envindi.html Joffre, O. M., Bosma, R. H., Ligtenberg, A., Tri, V. P. D., Ha, T. T. P., & Bregt, A. K. (2015). //***Combining participatory approaches and an agent-based model for better planning shrimp aquaculture. Agricultural Systems, 141, Jayantha, W. M., & Hui, E. C. M. (n.d.). Central Europe towards Sustainable Building CESB10 Prague Assessment Methods DETERMINANTS OF HOUSING CONSUMPTION AND RESIDENTIAL CROWDING IN HONG KONG. New England Complex Systems Institute. (n.d.). About Complex Systems NECSI. Retrieved April 21, 2016, from Salvini, G., Ligtenberg, A., van Paassen, A., Bregt, A. K., Avitabile, V., & Herold, M. (2016). REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling. Journal of Environmental Management, 172, Problem definition Question to asnwer Why to model? ABM Modelling exercise Results & discussion