Open-framework Multi-criteria Spatial- Decision Support System

Towards a 10-Step ‘Open-framework Multi-criteria Spatial-Decision Support System’ for Pro-poor Urban Planning:

Exploring and systematising methodologies for Praia (Cape Verde)

Ar. Jaime ROYO OLIDa and Prof. Dr. Ar. Benamy TURKIENICZb

a International Development Cooperation Officer, Infrastructure & Rehabilitation, European Union1 Delegation to Eritrea. [email protected]

b SimmLab, Nucleo de Tecnología Urbana, School of Architecture, Universidade Federal Rio Grande do Sul (UFRGS) Porto Alegre, Brazil. [email protected]

Abstract

The aim of this paper is to “explore” the handling of “urban knowledge” by means of a number of methodologies which could constitute a ‘Multi-criteria Spatial-Decision Support System’ (MC-SDSS2) for addressing poverty reduction in planning services delivery in cities of the ‘South’.

Where a SDSS is a closed all-in-one computational package, this paper refers to ‘Closed-framework SDSS’. These can solve ‘precise’ semi-structured decision problems. Since urban planning can comprise unstructured decision-questions which cannot be resolved by means of a fully programmable procedure, the proposed architecture is that of an ‘Open-framework SDSS’. In an ‘Open-framework’, technical experts intervene more than in a ‘Closed-framework’ in order to adapt the SDSS procedures in a cyclical manner to the analytical findings on a particular urban setting. This entails for the experts to iteratively identify possible intersections, integration possibilities and interrelations of different data and knowledge sources in order to formulate solvable semi-structured decision-problems.

The theoretical challenge addressed is first whether an ‘Open-framework MC-SDSS’ can be instrumental for assisting urban planning decision-making. Then, whether it can also be useful for tackling poverty reduction. For the latter purpose, an MC-SDSS must combine a number of unconditional technical procedures and accommodate “unorthodox and hybrid arrangements” such different participatory processes.

The cost of implementing an ‘Open-framework MC-SDSS’ will mainly depend on available knowledge and data-banks such as GIS, cadastre, census and on the cost of acquiring tools. These may include: statistical packages (i.e.: SPSS, GiveWin/PcGive, E-views or Microfit), geo-referencing systems (i.e.: open source GV-SIG, or proprietary ARC-GIS), technical-environmental models and infrastructure multi-criteria assessment tools looking into poverty reduction (i.e.: those developed by Ove Arup and Engineers Against Poverty, FIDIC, IUCN, DfID or OECD). The case is exemplified by master-planning methodologies used by the Brazilian UFRGS’s Nucleo de Tecnologia Urbana for Canela and a number of operations applied for Cape Verde’s capital city Praia. The 10 steps are exemplified using a Praia’s set of statistical and geographical information.

1 �� U�D��g�� V�� U�D��g�� .2 SDSS �� f�� ‘P��g S�� S��’ (PSS).

Introduction

Master-planning in many developing cities remains essentially a regulatory exercise, in the sense of the modernist approach of conceiving top-down urban plans. This is mostly achieved by means of a set of spatial depictions of strongly biased politically or technically-motivated decisions with limited analytical considerations. In a city like Praia, capital of Cape Verde, for instance, The Plano Director Municipal 2008-20203 (hereinafter PDM) or ‘Municipal Master-Plan’ was essentially constituted by basic Computer Aided Design (CAD) representation of a number of infrastructural proposals complemented by various regulation recommendations (see fig.1). Some particular findings on topographical and property ownership also played a significant role in the PDM. Praia’s PDM explicitly challenges previous urban policies which had deteriorated the gap between the social services provisions in consolidated neighbourhoods and in marginal quarters. Yet, the PDM does not ground its recommendations on analytical methodologies which substantiate nor could assist in offering consistent alternatives.

Ambiguous formal representation approaches to Master-planning do offer ample margin for decisional arbitrariness be it in political or technical terms. Municipal offices are in general not technically equipped to model the impact of potential scenarios and most often are not motivated to share knowledge on possible predictions with the public. Imagery visualisations are generally used to mediate and/or communicate ideas to the public. Whereas spatial-drafting can play a sensible role in the process of urban planning, it can further benefit from the use of analytical and modelling tools which are gradually becoming more affordable. For instance, recent advances in land-use models, Geographic Information Systems (GIS) and Multi-Criteria decision-making (MCDM) are enhancing the manageability, analysis, production and establishment of correlations between technical and inhabitants’ knowledge.

The complementary integration of technical models and tools in planning in the ‘South’ is exemplified by the ‘Master-Plan for the localisation of social housing in Canela, Brazil’4 undertaken by the Nucleo de Tecnologia Urbana and Simmlab5 of the Unversidade Federal Rio Grande do Sul (UFRGS). In contrast to Praia’s PDM, Canela’s spatial planning decisional procedures followed a number of complementary methodologies which can be clearly comprehended. Analytical findings on poverty-inducing factors, participatory needs assessment and a number of socio-economic diagnoses are integral to the Master-plan and illustrated in a didactic manner.

3 “P�� D�� M�� P�� 2008�2020”, �� M�� f P��, (2008). 4 B�� TUKI�NI�Z. “P�� L�� H��çã� �� I�� S�� (PLHIS), �AN�LA, R�� G�� S��”, NTU�S��, U��v�� F�� R�� G�� S�� , (UFRGS), P�� A��g�� (2008).5 S�� www.simmlab.ufrgs.br

[fig. 1] Standard approach to urban planning by municipal technical office of Praia.

(Source: Jaime Royo Olid)

The problem in question is therefore, how can master-planning involving multi-layered diagnosis and analytical approaches, such as exemplified for Canela, be further systematised to make sure its extrapolation to other cities facilitates the incorporation of instrumental knowledge for poverty-reduction.

Types of Decision-questions and ‘Systems for Supporting Spatial Decision’

The systematisation of planning methodologies is not a new issue and has been subject of much research in urbanism, regional planning, in social sciences in general. It has even been explored in computer-based ‘Systems for Supporting Spatial Decision’. Technologists and researchers have developed so-called ‘Decision Support Systems’ or DSS for over 40 years6. As computerised models became more advanced, research focused on including multi-criteria, optimisation and simulation models. The idea of model-driven ‘Spatial Decision Support Systems’ (SDSS) evolved in the late 1980’s7 and by 1995 the SDSS concept8 had become firmly established in literature.

It is said that a decision is structured when the variables considered are limited, known and can be automatically processed. These can be assisted by so-called Spatial Data Processing Systems (SDPSs). An unstructured decision9 is based on situations which cannot be generally modelled, due to unmanageable complexity or because they are wicked or ill-defined problems, and therefore must be resolved by means of users based on their expertise and experience –this is considered the case for the conventional approach to planning as described above for Praia’s PDM.

There is an intermediate scenario of semi-structured decisions where a computer can assist the decision-maker. Semi-structured decisions are solved by combining a number of automated procedures with the required adaptive modifications to suit the procedures to particularities of the question undertaken by an expert.

An Spatial-DSS thus tends to address situations where a semi-structured spatial planning decision is assisted by analytical findings of Geographic Information

6 D�� J. P�W�R, R�� SHARDA, “M��v�� : �� ”, D�� S�� S�� 43 (2007), 1044�1061.7 ARMSTR�NG, D�NSHAM, �� RUSHT�N, “A �� D�� S�� S�� f�� L�� P��g: ��g�, �� ”, 1986. Av�� f��, www.mapcontext.com 8 M.D. �R�SSLAND, B.�. W�NN�, W.�. P�RKINS, “S�� D�� S�� S��: �� v��v�� f �� M.D. �R�SSLAND, B.�. W�NN�, W.�. P�RKINS, “S�� D�� S�� S��: �� v��v�� f ��g� �� f �ffi��”, �� D�� S�� S�� 14, 219�235, (1995).9 DSS: www.referenceforbusiness.com/small/Co-Di/Decision-Support-Systems.html.

[fig. 2] Example of one analytical sheet on ‘Areas with habitation problems’

(Source: ‘Master-Plan for the localisation of social housing in Canela’)

Systems (GIS) but, as described by Densham10, these do not adequately support decision-making per se. A suggested so-called ‘Sprague’s three-level framework for the development of a SDSS’, therefore, includes a computationally integrated set of tools: a generic “SDSS tool-box”, a “SDSS generator” which extracts the relevant components from the toolbox and a “Specific SDSS” which is adapted to solve particular problems (see fig. 3).

Sprague’s framework implies that the “adaptive modification” can be computationally programmed with the assistance of a ‘technical supporter’. Thus, to configure a “SDSS generator” the nature of the semi-structured decision questions have to reach a considerably deterministic level of precision. Sprague’s framework corresponds to what can be denominated a ‘Closed-framework SDSS’ since the decisional steps can be computationally processed in a closed architecture. This is an attractive model for private companies which can sell planning packages or services. ESRI11 is, for instance, producing comprehensive planning support modules for ArcGIS in line with SDSS logic, where decisional intricacies and affordability of the system remain subject to proprietary rights.

For situations of less deterministic semi-structured decisions-questions, such as for urban planning, the theoretical and practical challenge addressed in this paper is what extent a less rigid SDSS approach can be useful. The first problem one would face in trying to use a Sprague’s framework for urban planning is the impossibility of pre-programming the ‘SDSS generator’ module to address a set of semi-structured decision-questions which may, prior to benefiting from analytical findings, constitute an overall unstructured decision-question. The figure below illustrates a decision-question procedure which cannot be fully programmable but its subcomponents can be treated as complementary semi-structured decision-questions.

10 P.J. D�NSHAM, “S�� D�� S�� S��”, �� M�g��, D.J., G��, M.F. �� R��, D.W. G��g�� I�f�� S��: P�� A��, L��g�� (1991), L��, ��. 403�412.11 �SRI: ‘��v�� S�� R�� I��’, I��. �� k�� f�� f A��GIS http://www.esri.com.

Weighting of factors

[fig. 3] Sprague’s three level framework for developing SDSS (Densham, 1991)1

[fig. 4]. GIS process model for the evaluation of potential social housing locations.


5

considerably deterministic level of precision. Sprague’s framework corresponds to what can be denominated a ‘Closed-framework SDSS’ since the decisional steps can be computationally processed in a closed architecture. This is an attractive model for private companies which can sell planning packages or services. ESRI12 is, for instance, producing comprehensive planning support modules for ArcGIS in line with SDSS logic, where decisional intricacies and affordability of the system remain subject to proprietary rights.

For situations of less deterministic semi-structured decisions-questions, such as for urban planning, the theoretical and practical challenge addressed in this paper is what extent a less rigid SDSS approach can be useful. The first problem one would face in trying to use a Sprague’s framework for urban planning is the impossibility of pre-programming the ‘SDSS generator’ module to address a set of semi-structured decision-questions which may, prior to benefiting from analytical findings, constitute an overall unstructured decision-question. The figure below illustrates a decision-question procedure which cannot be fully programmable but its subcomponents can be treated as complementary semi-structured decision-questions.

[fig. 4]. GIS process model for the evaluation of potential social housing locations. (Source: ‘Master-Plan for the localisation of social housing in Canela’)

Thus, in order to adapt the SDSS approach to planning procedures such as applied in the case of Canela and described in the figure above, a framework is suggested here for interrelating semi-structured decision questions. In such a framework, possible computer-aided components of the system are not necessarily interlinked by an ‘SDSS

12 ESRI: ‘Environmental Systems Research Institute’, Inc. is mainly known for the creation of ArcGIS http://www.esri.com.

GIS

Adequacy levels evaluation for locating future housing

Verification of the theoreticalmodel with real data: level of adequacy ofexisting settlements

Theoretical model for the identification of appropriate

location for social housing

Geographic data gathering and adaptation

Definition of spatial factors on adequacy levels for locating social housing

Weighting of factors

Production of a map appropriateness for locating social housing

Overlay of informal settlements with existing map of appropriateness

Evaluation of current situation of informal settlements

Evaluation of the adequacy of non-urbanised land for new social housing

hus, in order to adapt the SDSS approach to planning procedures such as applied in the case of Canela and described in the figure above, a framework is suggested here for interrelating semi-structured decision questions. In such a framework, possible computer-aided components of the system are not necessarily interlinked by an ‘SDSS Generator’ but rather by an expert or group of experts12 –preferably multidisciplinary– who would assess the integration potential and relevance of knowledge sources and analytical findings. The interoperability of outputs between tools will obviously cause challenges. It is proposed that the rationale for the selection of particular SDSS tools, the knowledge banks and any other analytical findings be publicly displayed. Both for its public openness and because its operations would not involve a computer-closed architecture, we call it ‘Open-framework13 SDSS’ represented as follows:

12 T�� v��v�� f �x�� DSS ��g� ‘�x�� S��’ �� f�� K��g�� g�� DSS �� T�� A. AR�NTZ�, A�� W.J. B��g��, �� H�� J.P. T��, �� “T�� I��g�� f �x�� k��g� �� D�� S�� S�� f�� F�� L�� P��g”, ��., ��v��. �� U�� S��, V��. 19, N�.4, ��. 227�247 (1995).13 J. MAL�Z�WKI ��g�� ‘��g��’ �� ‘��’ M��SDSS �� f �� GIS ��k�g� �� g�� f�� v� �� x�� f��, “GIS �� M�� D�� A��”, J�� W�� S��, �.392, N�� k, (1999).

[fig. 5] Authors’ suggested ‘Open-framework’ for developing a SDSS based of Sprague’s Three-level framework.

Composing an ‘Open-framework MC-SDSS’ for pro-poor urban planning

Step 1: Constituting a ‘Knowledge-Bank’

The major challenge for an ‘Open-framework MC-SDSS’ is how to integrate a number of separate structured and semi-structured decision-making questions to address a complex or unstructured planning decision problem. If we are to address global objectives such as poverty reduction, setting workable specific sub-objectives will require a number of corresponding preliminary analysis which will reveal specificities of the site. In the case of Praia, for instance, its topography of volcanic origin highly affects the urban stratification of social classes. This is the case of the steepness of the slope, and the location of property vis-à-vis floodable dry-river basins.

However, there may be several other factors affecting the stratification of social classes. Stage one, in setting an ‘Open-framework MC-SDSS’, is therefore the building up of a knowledge-bank whereby not only data is gathered but analysis is undertaken and transparently documented such as through a dedicated public website. Preliminary analytical findings may be based on demographic trends, to the cadastre, technical information on social infrastructure, participatory needs assessment, etc... The more geo-referenced the data the more subsequent spatial analysis can be automatically processed. Geographic Information Systems (GIS) offer more and more applications which facilitate the integration of data. Even direct on-line participatory GIS have been

[fig. 6] Proposed architecture for an ‘Open Framework MC-SDSS’ for Pro-poor Urban Planning.(Source: Jaime Royo Olid as systematised and extended from ‘Master-Plan for the localisation of social housing in Canela’)

produced14. GIS can even be found open-source such as the EU-funded gvSIG15. Thus, the cost of building a SDSS will depend to a great extent on the constitution of the necessary data basis.

Public institutions dealing with statistics are crucial for this phase. Many would use statistical packages which can be used to correlate behavioural or preferential responses to spatially-located characteristics through statistical regressions. Such software may include basic Excel use, ‘Statistical Package for the Social Sciences’ (SPSS16), E-views or Microfit17, or the simpler-to-use econometrics package GiveWin/PcGive18. Let us therefore assume that by means of surveys and the use of a particular statistical software we obtain a hierarchical list of inhabitants preferences on infrastructure services such as access to social housing, water and sanitation and social infrastructure.

Developing a participatory priorities list should be considered as an imperative component of the ‘Open-framework MC-SDSS’ since municipal services would usually be run by the local elites who may not favour lower classes needs.

Step 2: Urban Environmental Mapping of Inferences for Evolving a Local Agenda

Once the ‘Knowledge-Bank’ created, from a technical and ideally non-pollicised perspective, the ‘Technical Supporter’ should examine the level of correspondence between findings and the local political agenda –which may not have taken account of inhabitants’ priorities.

14 T�� GIS W�� f W��GIS �� v� ��k� T�� GIS W�� f W��GIS �� v� ��k��g �� fi�� “��” �� f�� g � �� k�� . http://www.participatorygis.com; �SRI �� v��g A��GIS �� v�� f �� f��g ‘��’ �� : http://www.esri.com/software/arcgis/arcgisonline/index.html. 15 T�� gvSIG �� 2004 �� f�� g�� f �� f�� T�� gvSIG �� 2004 �� f�� g�� f �� f�� g� �� f �� R�g�� M�� f I�f�� T�� f V�� (S��). �� A�� . www.gvsig.org 16 S�� 2009 SDSS �� ‘P��v� A�� S�f��’ (PASW). S��: http://www.spss.com 17 M��fi � �� v�, ��v�� g�� f f�� f�� g, �� M��fi� �� v�, ��v�� g�� f f�� f�� g, �� g, f��g, �� g, fi�� g��, �� g�� . S��: http://www.oup.co.uk/microfit 18 P�G�v� �� v� ��v�� g�� f�� , ��, �� fi�� . S��: http://www.pcgive.com

[fig. 7] Example of SPSS-extracted graphs on marginal neighbourhood inhabitants’ perception of urban problems in Praia.

(Source: Survey by Cristobal DELGADO MATAS and Jaime ROYO OLID in Praia, 2008)

Let us assume that the local agenda corresponds to the framework suggested by the UN-Habitat ‘Urban Management Programme’ for Environmental Mapping described below, which is a useful index for mapping analysis in view of delivering social services. Such framework should however be complemented of adapted to findings on inhabitant’s specific knowledge which may reveal additional potential mapping parameters. In other words, the ‘Open-framework MC-SDSS’ Technical Supporter has to find complementarities between local knowledge and any imported framework.

Analytical Framework for Urban Environmental Mapping19

Urban environmental mapping can be most useful by means of GIS although alternatives should not be excluded. This can be a never-ending task in the sense that we can always add more precise or additional layers of data. Nonetheless, a limited number of basic operations, which can be managed in a personal laptop, can suffice to identify a site-specific particularities relevant to start fine-tuning decision-questions. The following sequence of figures illustrate GIS operations which can be derived by means of geo-referencing a standard CAD contour and infrastructure map of the city. Obviously, there more advanced and complete GIS (and associated data-basis) information on infrastructure the greater accuracy can be achieved in the analysis.

19 S��: T�� 21.1 “��v�� P��g f�� g�� f�� D�v��”, �.380, UN�H�� , (2003).

Analytical Framework for Urban Environmental Mapping20

UrbanEnvironmental Indicators

Parameters for Mapping Inferences for evolving local agenda

-Population density in different wards of the city. -Indicates areas requiring de-densification or densification. -Population changes in different wards over past decades.

Population

-Number of households and sex ratio in each ward. -Highlights needs for improving/strengthening social infrastructure.

Housing -Number of dwelling units in each ward.

-Dwelling conditions- Number of persons per room-ward-wise.

-Location and number of slums.

-Level of services in slums (per capita availability).

-Average house rents and land prices in different areas/wards of the city.

-Housing supply by government/public/private sector in the city.

-Availability of affordable housing, crowding and living conditions. Mismatch in demand and supply of housing across the city.

Water Supply -Sources of water supply (surface/ground), including community-based sources (hand pumps, wells).

-Location and capacity of water treatment plants.

-Average per capita supply (at city level and different wards)

-Areas in the city facing acute shortage of water and with poor quality of drinking water.

-Total supply and consumption of water for different uses in all the zones/wards of the city.

-Water supply network showing trunk lines, distribution lines etc.

-Zone-wise /ward-wise number of connections (for each type of use).

-Spatial availability and quality of potable water in the city. Highlights areas having excessive consumption of water and requiring conservation measures. Estimation of total available quantity and consumption of water will suggest the population that the city can support for sustainable development.

-Total quantity of sewage generated in the city. -Location and capacity of sewage treatment plants.

Sewerage & Drainage

-Mode of disposal of (un)treated sewage (land/surface water etc.). -Zone-wise /ward wise number of individual connections, number of public latrines in each slum/community group.

-Sewage network in the city.

-Topographical map of the city depicting prominent water-logged areas and all the open drains.

-BOD and DO values for all drains, stream or river passing through the city.

-Suggests areas in the city requiring sewerage facilities like public latrines, septic tanks, etc. and drainage facilities. Poor availability of sewerage facilities and untreated sewerage create unhygienic conditions affecting the health of urban citizens.

Solid Waste -Total and per capita generation of solid waste.

-Mode of disposal of solid waste (land filling, composting, incineration, etc.).

-Collection and disposal of hazardous industrial waste, hospital waste, abattoir waste, etc. -Zone-wise/ward-wise generation and collection of solid waste.

-Highlights areas with uncollected garbage and facilities for disposal of hazardous industrial and hospital waste.

Transport -Growth of vehicles and road network in the city.

-Peak hour traffic volume on major roads.

-Accidents on major roads.

-Accident-prone areas and bottlenecks on different corridors.

-Public transport routes and residential areas well connected with major commercial centres, office complexes, by public transport.

-Identifies roads requiring widening and / or better traffic management and suggests the need of remedial measure in different parts of the city.

Green spaces -Location and area of forests, public parks and other green spaces in the city.

-Temporal variation in the green cover of the city.

-Action for saving trees in certain pockets of the city and areas devoid of any vegetation.

Air pollution -Ambient air quality in the city (at different monitoring stations). -Emissions from transport sector identifying polluted corridors during peak hours.

-Delineation of areas exceeding ‘prescribed’ air quality standards. Measures to reduce air pollution menace on polluted corridors and use of appropriate technology to reduce industrial air pollution.

Noise pollution -Ambient noise levels in commercial, industrial and residential areas near hospitals.

-Peak hour noise levels at major roads intersections.

-Helps identify the causes of noise pollution in different areas and to evolve measures for reducing them.

20 Source: Table 21.1 “Environmental Planning for integrated urban infrastructure Development”, p.380, UN-Habitat publication, (2003).

[fig. 8] Satellite view of Praia in 2009, Santiago Island, Cape Verde.

(Source: Google Earth)

[fig. 9] Elevation raster produced with GV-SIG or ARC-GIS illustrating a volcanic-generated topography.

(Source: Jaime Royo Olid-derived from Praia’s Municipality’s contours CAD map)

[fig. 10] Categorised elevation raster (for visual clarity) where white area reflects optimum altitude for water provision which has to be pumped from sea level.


Step 3: Urban Environmental Profiling

The GIS mapping of the topographical properties of Praia reveals the extent to which relief fragments and limits potential urban expansion. In addition, if we address the first UN-Habitat’s framework ‘Urban Environmental Indicator’ on population we see that the topographical constraints could represent a major barrier to Cape Verde’s statistical office’s (INE20) estimation on the increase of urban population from 2010 to 2020 which will be around 31%. Accordingly, Praia’s PDM foresees that the share of urban growth will be in the order of 2 to 3% per annum growth rates which would imply a growth of 12.6% to 20% between 2010 and 2020.

20 I�� N�� : www.ine.cv

[fig. 11] Elevation raster. Black: slopes >26% considered physical barriers or inappropriate for low-cost construction.

(Source: Jaime Royo Olid in analogy to Canela’s Master plan)

[fig. 12] Dark green: hydrological lines. Mid-green: agricultural land, forest and urban green spaces. They all follow basins.

(Source: Jaime Royo Olid as derived from PDM))

Year 2010 2011 2012 2013 2014 2015 2020Population at 2%

growth per annum since 2008

119,916 122,316 124,762 127,257 129,802 132,398 135,056

Population at 3% growth per annum

since 2008125,461 129,225 133,101 137,095 141,208 145,444 149,807

Considering a potential increase of 20%, the subsequent decision-question for the Technical Supporter could be to address whether there is enough non-built land to accommodate the expected corresponding necessary infrastructure at the current density levels and growth patterns.

By adding a number of zoning constraints to the previous analysis, the figure below reveals that suitable available land for building (in white) is highly constrained. Whereas there are not too many choices for the location and restrictions associated to the international airport in such topography, the allocation of restricted areas for touristic developments could be object of further research. These areas are called “Zones for Tourism and Industrial Development” (ZDTI) and have frequently been object of controversy in terms of lack of transparency, expropriations, zoning requalification, tax exemptions and in terms of questionable socio-economic benefits obtained. Whereas in the Island of Sal tourism represents a major source of income, in Santiago island most privatised allotments remain un-built. The Technical Supporter, who should be politically independent, could raise questions about the extent to which these privatisations may actually be used to privilege particular people or even artificially induce higher land-values in Praia.

The following figure reflects in white the land available for building as derived from topographical considerations and functional zoning. The next stage in the process consists on the more detailed analysis that land as per the operational municipal district areas (hereinafter UOPGs).A superficial GIS analysis of UOPG 2, Palmarejo and Cidadella –recently developed and sparsely occupied–, reveals plentiful urbanised21 yet un-used land. Less than ten

21 P��, �� A�� S�� F�� f�� P��, �� A�� S�� F�� f�� 7�� D�v�� F��.

[fig. 13] Population growth prediction for Praia 2010-2020.

(Source: Praia’s 2008-2020 PDM)

[fig. 14] Overlay of zoning for airport and privileged locations for touristic development (ZDTIs). How do ZDTIs influence land prices in Praia worth exploring!

(Source: Jaime Royo Olid as derived from PDM)

years ago most allotments were sold to high-middle classes (locals and Diaspora) who have in part been speculating with the –until recently– increasing land-values and only some have built. Interestingly, the corresponding urbanisation project’s report describes how the prescribed creation of a land-allocation committee was never undertaken. Consequently, the current low-levels of occupation may imply considerable opportunity-costs for the share of Praia’s inhabitants who live in areas without basic infrastructure. Following the recent global financial crisis and associated collapse of real-estate bubbles, the Technical Supporter may find scope to further investigate potential property renegotiations in order to favour mix-use allocations for this area rather than the current status of exclusively middle to high-class residential neighbourhoods.

UOPGs 3 and 10 clearly contain the most available ‘suitable’ land on the basis of the limited criteria analysed above. Where UOPG 3 would have to be further evaluated in terms of its isolation/distance from the main urban social services, UOPG 10 already benefits from schools and a health centre. However, the latter may be suffering from a degree of social marginalisation and noise pollution from the airport which would require a participatory diagnosis of inhabitants’ perception parameters. Thus, additional detailed analysis, adapted to each ward, would be required to identify appropriate land and circumstances for new services and housing.

[fig. 15] Raster where white indicates suitable land for building. Rectangles indicate districts for further detailed analysis. (Source: Jaime Royo Olid)

[fig. 16] Identification of potentially suitable terrain for future new growth of Praia.


Step 4. Spatial & Sectoral grouping of interrelated decision-questions

From the previous analysis we can establish that in order to accommodate the expected demographic growth, Praia needs to either renegotiate zoning provisions for ZDTIs or facilitate mix-use developments in non-built but urbanised areas (such as in Cidadella and Palmarejo) or alternatively focus on the restructuring potential of built urban areas. Probably a combination of all would be necessary though. If redefining ZDTIs is not a political option and turning Cidadella into a mix-use are is not legally plausible, the ‘Technical SDSS Supporter’ will reasonably proceed to analyse the properties of built and urbanised areas through different types of ‘Urban Environmental Indicators’ such as the spatial distribution of social infrastructure, growth patterns in different quarters, transportation infrastructure features. At this stage, a new decision-question can arise concerning how to identify adequate locations for housing (preferably social) for accommodating the expected demographic growth within the existing urban fabric. This needs, in turn, to address a number analysis and semi-structured decision-questions from various perspectives. The following figure shows the grouping of different thematic analysis (i.e.: transportation access levels, spatial distribution of social infrastructure, land values, etc...) as developed for the social housing master-plan of Canela.

Once the schematic approach to grouping analytical layers and decision-questions is determined, an added-value feature for any reader of the information is its visual illustration. The synthesis of the procedure described in the figure above can be represented in a geo-referenced map where each pixel is allocated a value in function of the level of ‘restriction’. In order to achieve this, the Technical Supporter would have to establish weighting of the different layers (procedure described in Step 6). The figure below reflects how the semi-structure decision-question on identifying land ‘adequacy levels for social housing locations’ involving all the layers described above can be synthetically represented.

19

RESTRICTIONS

TopographyGlobal Integration(transport system)

MOBILITY(Attrition Map)

HAZARDOUS AREAS(High slopes, flood prone,…)

Public TransportPRESERVATION AREAS

(River catchment areas,…)

Education infrastructureACCESS

To public education

Health infrastructureACCESS

To public healthBUFFER ZONES

(Margins along roadways,…)General consumption

infrastructureACCESS

To basic shops

ACCESSPARKS

Employment centresACCESS

To employmentLEGAL AND ENVIRONMENT

RESTRICTIONS

Water ProvisionSanitation ProvisionSolid waste collectionElectricity sources

INFRASTRUCTURE

Water drainagePavement of urban ways

ADEQUACY LEVELS FORSOCIAL HOUSING

LOCATIONSMunicipality map on Land

valuesLand Values

Strategic Development Plan Urban Zoning

[fig. 17] Multi-layered model for identifying suitable land for social housing in Canela. (Source: ‘Master-Plan for the localisation of social housing in Canela’)

Once the schematic approach to grouping analytical layers and decision-questions is determined, an added-value feature for any reader of the information is its visual illustration. The synthesis of the procedure described in the figure above can be represented in a geo-referenced map where each pixel is allocated a value in function of the level of 'restriction'. In order to achieve this, the Technical Supporter would have to establish weighting of the different layers (procedure described in Step 6). The figure below reflects how the semi-structure decision-question on identifying land ‘adequacy levels for social housing locations’ involving all the layers described above can be synthetically represented.

[fig. 17] Multi-layered model for identifying suitable land for social housing in Canela.


Step 5. Gathering Sectoral Quantitative Models

In order to extrapolate Canela’s model for the identification of adequate locations for social housing for the case of Praia, the Technical Supporter needs to take into consideration local-specific settlement growth patterns. In other words, having undertaken previous analysis, adapted models have to be identified or constructed

If we represent Praia in its origins, we can see that it grew from the Plateau overlooking the Praia de Santa Maria (thus the city’s name) which in the figure below stands clearly like an island–like table top at about 30 meters in altitude above and surrounded by the delta’s of the dry river basins. Subsequent settlements appeared following an analogous development pattern starting on the plateaus (or Achadas in Portuguese) where first occupied by institutional buildings and by the elites.

[fig. 18] Multi-layered synthesis analysis map on ‘Adequacy levels for social housing locations’.


As population started to grow after an almost three decade slow recovery from the World War-II famine, settlements started to grow on the slopes of the achadas as close as possible from the main centres of activity and existing infrastructure.

As the most attractive locale have been occupied, during the last twenty years informal settlements have gradually invaded urban basins and progressively climbed other less accessible achadas such that of Eugénio Lima22 illustrated below.

22 T�� v�� v�� f A�� gé�� L�� v� �� g�� M�� T�� “�� gé�� L�� B��x� �� P��, �� V��”, Q�� IUAV 40, U��v��à I��v �� V��z��, M�� Pv�, (2005).

[fig. 19] Praia in 1960’s in GIS analysis raster context. Light green areas are subject to flooding.

(Source: Jaime Royo Olid as derived from ortophotograph from Ministry of Infrastructure)

[fig. 20] Informal housing sprawling downwards from Achada de Santo Antonio.

(Source: Jaime Royo Olid 2008)

The informal growth of settlement patterns in Praia occupied about one fourth of the urban area. Many of these settlements are highly susceptible to landslides and flooding during the seasonal rains.

From the previous findings the ‘Technical Supporter’ can establish further correlations between habitation precariousness and particular geophysical factors. Thus, a model on the adequacy of locations for social housing for Praia should integrate those features: slope, geotechnical characteristics, flood-prone parameters, height vis-a-vis sea level in order to benefit from water, etc. A logistic model with the purpose to estimate the appropriateness of land can be extracted by using a binary logistic procedure in SPSS or other statistical packages such as GIveWIn/PcGive, E views or Microfit. From primary observation we confirm that on sloping areas the built grain is smaller. By crossing data we may find out a particular correlation between precise size limits to given slopes.

[fig. 21] Achada Eugénio Lima, Praia.

(Source: Ortophotograph KLM flight 2003)

[fig. 22] Typical land-slide during summer rains in Praia, August 2008.


The previous illustration reveals the correlation of housing of social classes and their location with regards to urban basins and achadas. Smaller houses and shacks indeed occur in the basins and slopes up to 35%. Categories range from houses of less than 50m2, between 50m2 and 100m2, and of more than 100m2. The process of categorization of houses according to size is done in a combination of trial and error and on site observation in order to estimate whether ranges reflect reality.

Step 6: Adapting Quantitative Models

Steps 4 and 5, which consisted on the overlay or grouping of decision-questions can lead to the definition of a model correlating a number of analytical factors which affect the level of suitability of land for social housing. In some cases, that level of suitability can be quantified by attributing a weighting to the different parameters analysed. The total adequacy level is then defined as the sum of the respective performance levels of each analytical layer. For that purpose, the ‘Technical Supporter’ may explore the weightings of pre-existing models which have been produced for similar purposes elsewhere. The attribution of integrated weightings is a combination of quantitative results with qualitative judgements.

[fig. 23] Shapes diagram of buildings classified by assumed inhabitants socio-economic Level in Praia, produced with open-source gvSIG.

(Source: Cristobal Delgado Matas and Jaime Royo Olid)

[fig. 24] Schematic diagram: multiplying factors by attribute relevance share for suitability to social housing.


Let us assume that following the application of the various adapted models to the case of Praia, given the limitations of land available for new social housing in proximity of pre-existing social services, access to water and sanitation, that one set of areas of the city to be prioritised for planning intervention were the areas with unconsolidated infrastructure but yet inhabited. Given the ad-hoc structure of informal settlements which do not take in consideration basic access route needs, nor hydrological lines, these areas present the greatest potential benefits in being restructured23. Restructuring there on the one hand can reduce the potential for hazards such as land-slides and fatal flooding and on the other accommodate higher density levels of inhabitation. Thus, we may grant higher relevance to informal settlements’ in addressing the selection of investments for restructuring.

The GIS illustration below indicates the location of the main informal settlements in Praia from which we can confirm the close correlation with high-slope, and level of proximity to social infrastructure for the occurrence of spontaneous settlements24. Compared to the case of Canela (see fig.26), the parameter of slope in Praia shall also be granted greater importance when identifying land to be restructured25.

23 T�� f ��v�� v� �� f f��g �� 8�� 9�� D�v�� F�� g� �� D��g�� P�� f �� NG� Af��’70 �� M�� f P��. 24 T�� fi �� f �� f�� f�� g �� T�� fi�� f �� f�� f�� g ��k�� NG� Af��’70 �� P��’� M��. T�� fi�� g�� f �� 50 �2 �� f �� v�� x�� g. M�v�� Af��’70, �á�� M�� P��, “A R�q��fi��ç�� U�� B�� V��: U� �� P��”, A�f��ç�� L��, P�� (2005).25 F�� g �� f ��f�� D. Z. L�Ã� �� S. Z. L�Ã�, “S�� f��çõ�� g��g�áfi�� óg�� g��: � �� G��v��í, RS”. A�� XIII SBSR –S��ó�� B�� S�� R��, F��ó��, S�, (2007), INP�.

[fig. 25] Location of areas on unconsolidated infrastructure and informal settlements in Praia. (Source:Jaime Royo Olid1)

Step 7. Select poverty-reduction Multi-criteria Assessment Matrix

Once one of the decision-questions is assessed pointing at locations which can be object of interventions such as urban services delivery projects, the new question for the Technical Supporter is how to pre-assess the potential poverty-related impacts. Whereas there is no short-cut to assess project impacts the following tools26 can be instrumental in terms of methodological clarity:

-Aspire27 (by Ove Arup and Engineers Against Poverty);-PSM: Project Sustainability Management28 (by FIDIC);-Resource Kit for Sustainability Assessment29 (by IUCN);

26 T�� v�� “ASPIR� R�� D�v��: A S�� P�v�� I�f�� R�� f�� v��”.27 A�� f�� f�� g �� f ��f�� v�� v��g ��v�. http://www.oasys-software.com/products/sustainability/aspire/

28 PSM �� v� ��f�� v�� MDG� �� f��g “��g��, ��v��, �� v��”. http://www1.fidic.org/re-sources/sustainability/psm_paris_pgb_8feb05_final.ppt 29 T�� ‘I�� U�� f�� v�� f N��’ (IU�N) ‘S�� A��’ “��g� ��v�� f ��g�g��g ��k�� fi��g �� k�� ff��g �� v��, �� f ��g �� g �f �� g�� g�� f��k”. http://www.iucn.org/knowledge/monitoring_evaluation/tools_and_resources/#2

[fig. 26] Slope-raster for Canela and surroundings. City located in areas of mostly 0-10% slopes.


-SL Framework: Sustainable Livelihoods Framework30 (by DfID);-Promoting Pro-poor growth: Ex-Ante Poverty Impact Assessment31 (by OECD);-SPeAR®: Sustainable Project Appraisal Routine32 (by Ove Arup).

These have been widely used internationally at different stages of project-cycle management and therefore provide a number of examples to compare. Whereas these tools should not be considered as ultimately conclusive, they can be useful to promote a number of sustainability and poverty-related considerations which tend to be ignored by decision-makers.

For the purpose of this exercise we have selected Aspire which is infrastructure and poverty reduction focused and was developed most recently. It incorporates a number of features of the other multi-criteria assessment tools listed above such as a clear inter-phase and a focus on ‘Millennium Development Goals’33.

Step 8: Multi-criteria evaluation of Decision Proposals

Although unconsolidated quarters of the city of Praia constitute an challenge to any strategy which considers the city’s forthcoming demographic growth, major municipal investments in recent years have mainly favoured interventions transportation infrastructure. Even the current Praia’s PDM, which challenges previous practice, suggests eight out of the fourteen major city re-structuring proposals to focus on new roadway and associated infrastructure. Yet, traffic levels do not seem to be neither a pressing nor a structural problem for Praia. Among the road interventions, asphalting stone-paved roads has been a generalised trend. This practice follows an apparent strive for modernity which substitutes local, labour-intensive modes of construction by imported material which require also imported machinery. This has been substantially increasing the national budget deficit and reduced employment opportunities of stone-masons. In addition, asphalt requires high-capital investments for maintenance every four to six years. From an economic, poverty-reduction and environmental sustainability point of view, the sacrifice of local labour-intensive stone paving deserves further study and could object of a decision-question for national economic practices. Yet, no governmental department seems worried about it. Given the embedded lack of economic and environmental sustainability of the asphalting process in the context of Cape Verde, it may be essentially associated to the interests of particular groups who benefit from the imports. A sensible decision-question for Praia would be whether such practices are advisable from a socio-economic point of view.

The previous decision-question can be exemplified by addressing the major road investment in Praia in recent years which has been the USD 35 million Ring-Road34. This four-lane road was justified as an essential corridor for the potential expansion of the city and to reduce traffic levels in its inner major road axis. However, from both our GIS analysis and experience of the road during its first year of existence, we can appreciate that the road does not seem to follow any area suitable for future extension of the city and that the path is so sinuous that it that it does not constitute a shorter or faster alternative to other major urban roads.

30 T�� SL f��k �� , �� , �� f � �� f �� ff�� v�� f�� v�� . http://www.eldis.org/go/livelihoods/ 31 ‘�x�� P�v�� I�� A��’ (PIA) �� v�� 2007 �� A�� D�v�� B��k’� ��k �� v�� , �� P�v�� S�� I�� A�� (PSIA) ��. I� �� “�� k�� , �� x�� PIA” �� “��gg�� x�� f��k”. www.oecd.org/dataoecd/46/39/38978856.pdf .32 T�� ‘S�� P�� A�� R��’, k�� SP�AR® �� g�� v�� 2001 �� g� ��g�� g� �� f �� g �� f �� v��, �� f �� , �� ..33 A g�� v� �� g�� v�� g�� 2015 ��g�� . S�� www.un.org/millenniumgoals. 34 P��’� R��g R�� USD 35 �� v�� USA’� ‘M�� g� ��’ ��: http://www.mcc.gov .

The following illustration shows the Ring-road with no traffic and the rocky hill side with readily available stone hardly used for new infrastructure construction in Cape Verde. The average number of vehicles counted in either ways in five trips through the whole road was 8 per trip. From the figure above it is evident that drivers will have to be ready to drive considerably longer distances if they are to use the Ring-Road.

With any serious Eco-Fin analysis or credible traffic study would have most probably found difficult to justify the investment and from the location of the road it is questionable whether traffic levels will increase much in the future such as to be economically viable to maintain. Its viability was possibly distorted by the fact that the road was the outcome of a grant. Figure 30 illustrates what could be an evaluation of Praia’s ring-road through the multi-criteria assessment tool Aspire.

The Aspire assessment tool includes about a hundred questions of qualitative nature divided into 20 subcategories in four general categories: economics, society, institutions

[fig. 27] GIS illustration of Praia’s infrastructure built between 2003-2010: pink are newly built buildings, orange new urban roads and blue the new Ring-Road. (Source: Jaime Royo Olid1)

[fig. 28] Empty Praia’s Ring-road, 08-2008: asphalt amidst stones (Source: Jaime Royo Olid)

and environment. Since Aspire has been conceived to process non-quantitative data it can be useful for obtaining a general depiction of a project’s outlook at different stages of the project-cycle-management. Obviously, the more accurate quantitative data, the more precisely can the hundred questions be addressed. Since the assessment tool grants importance to the incidence on demand of local labour-intensive jobs, on local materials, on the incidence of imports to the national economy, on mid-term maintenance costs, etc... we obtain an evaluation diagram where most parameters range between mid to worst case.

In contrast, during the construction of the Ring-Road, the Municipality of Praia placed the 1/20 of the Ring-Road’s investment on interventions in informal settlements. Restructuring these settlements is a pre-condition to potential future densification. The project for the requalification of the Bela Vista quarters, for instance, included a number of infrastructure and capacity-building activities with a high direct social impact. A part from the construction of new social houses, it also comprised of water and sanitation provision and nine stone-containment walls used for preventing land-slides, channelling natural water ways and structuring the pathways of the settlement. With a capital investment of 250,000 Euros for a catchment area of 2,400 inhabitants that provides us 104 Euros of capital investment per inhabitant and a sense of potential impact.

The equivalent capital investment made on the Ring-Road on this type of containment walls, water and sanitation facilities for informal settlement inhabitants –if we assume they are about 30,000– could have been of 833 Euros per inhabitant. With this investment per inhabitant and reduced costs by the use of gabions (rather than cement mortar) the linear distance of terracing per inhabitant could be in the order of 2-3 meters. Assuming that 60-90km of linear gabions could be built, these would be enough for the total non consolidated quarters of Praia. Since the construction of gabions requires basic technology, the corresponding skills would benefit the urban inhabitants who would be able to spontaneously continue their production. The presence of readily available stones could further reduce costs of gabion construction. The following illustration shows typical gabions and agricultural terracing in Cape Verde and their hypothetical use in the slopes of the urban basins of Praia.

The hypothetical urban terracing intervention described above can be pre-assessed through the Aspire multi-criteria assessment tool. The resulting assessment

[fig. 30] Possible approach to urban basing restructuring by analogy to agricultural terracing by the means of stone gabion.

(Source: Pictures and collage by Jaime Royo Olid)

diagram suggests that most parameters of such an intervention would be positively assessed.

Step 9: Formulation of Spatial Planning Decisions and Step 10: Master-Plan

The indicative procedures described in this paper can assist spatial decision-questions related to the availability, suitable location and form of interventions for accommodating future urban expansion but the finding may not suffice. Before such decisions could be sufficiently substantiated as ‘Formulations of Spatial Planning Decision’ (Step 9), these would benefit from investigating additional ‘Inferences for evolving local agenda’ from the UN-Habitat’s ‘Analytical Framework for Urban Environmental Mapping’– which could affect, modify or even cancel-out our preliminary conclusions. In order to attain the level of detailed knowledge and formulations to constitute a Master-plan (Step 10), the Technical Supporter would have to complete a sufficiently holistic set of such procedures.

Conclusions

Praia’s Master-plan 2008-2020 (PDM) presents 14 well-intended specific proposals which have involved insufficient prior analysis. These proposals presuppose or depend on a political will and subsequent capacity to divert urban investments from consolidated areas –which tend to be owned by the governing elites– to unconsolidated areas. This is likely to constitute an intrinsic political bottleneck. When decision-makers are confronted to the allocation of scarce resources for the many demands of a city, they need clear illustrations of how they can best justify investments. The methodologies examined for the identification of the best-suited locations for social housing in Canela are directly relevant and exemplary for their conceptual clarity, the integration of inhabitants’ knowledge and technical models. The suggested 10-Step ‘Open Framework MC-SDSS’ involves an extrapolation and adaptation of such methods for Praia and an attempt to integrate into such system pro-poor considerations as central to the procedures.

The analytical methodologies used in this paper, by means of limited data and processed in a personal laptop, demonstrate that an ‘Open Framework MC-SDSS’ approach can, from its inception, reveal features which can be directed towards poverty-reduction purposes. For instance, a preliminary finding shows how constrained is the potential growth of Praia both by topographical aspects as well as by zoning which favours touristic investments on privileged locations which remain mainly un-exploited. If made public, these would, from the start, raise debate upon the strategic

[fig. 29] Evaluations through Aspire multi-criteria assessment tool: Left: of Praia’s Ring Road. Right: Hypothetical gabion-based informal settlement re-structuring. (Source: Jaime Royo Olid1)

development and zoning policies. UN-Habitat’s ‘Analytical Framework for Urban Environmental Mapping’ sets a mappings list for other particular urban features to become apparent such as the unbalanced distribution of the water and sanitation networks. The multi-criteria assessment tool Aspire suggests that if it had been used for pre-feasibility evaluations it would be unlikely that Praia’s Ring-road could have been justified whereas the provision of infrastructure in unconsolidated areas of the city –a prerequisite for future densification– would have been favourably assessed. The essential outcomes of this theoretical exercise are not the actual accuracy of the actions but rather the relevance of the methodologies to pro-poor urban planning. The next step in this inquiry would involve the actual building up of a fully suited ‘Open Framework MC-SDSS’ for Praia and its subsequent testing in decision-making.

Acknowledgements

This paper is the result of an MSc in ‘Regional Planning and Urban Design’ organised in UniCV of Cape Verde by a Brazilian-funded cooperation project run by CAPES. The project has been coordinated by Prof. Benamy Turkienicz and involved a number of academics, all from the UFRGS of Porto Alegre. The authors are particularly thankful to Dr Simone Leão who was instrumental for developing and teaching GIS methods referred to in this paper. Support was also appreciated from Ar. Vaneska Henrique and Mr. Rodrigo Lersch also from UFRGS. This research has also benefited from the support of the European Commission in facilitating research sessions. Mr. Cristobal Delgado Matas has also been instrumental to a number of analyses undertaken in this research.

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Open-framework Multi-criteria Spatial- Decision Support System