Presentation on theme: "Spatializing knowledge in urban governance Isa Baud and N. Sridharan University of Amsterdam School of Planning and Architecture, Delhi."— Presentation transcript:
Spatializing knowledge in urban governance Isa Baud and N. Sridharan University of Amsterdam School of Planning and Architecture, Delhi
Knowledge in urban governance? Urban governance requires networking for steering development processes Knowledge required to support processes: providing legitimacy, relevance, accountability Spatializing knowledge links variety of knowledge sources to one locality – integrated understanding Focus on potential of tools for more participatory urban governance
Urban governance networks From hierarchy to network? Theoretically, but complexity in practice Re-scaling – Brenner Urban regimes: with whom does the government work? Growth coalition of government and private sector Government and citizen networks for QoL issues Intra-government hierarchy for economic growth and infrastructure investment
Governance models. Network governance Market governance Hierarchical governance Basic principleReciprocityCommercial Exchange Political and Administrative Power Coordination principle CollaborationPriceRules Roles of government Govt. as partnerGovt. as enabler, setting standards and contracting out Central ruler (different levels) Key values Collaborative decisions on distribution issues consumer choicePublic goods
Types of knowledge and exchange TacitContextual- embedded knowledge; technical, economic Contextual- embedded knowledge; community- based Contextual – embedded knowledge: political and network levels Codified knowledge (analytical, regulatory, standards) Main actors Individual experience Professional knowledge among sector professionals Community knowledge spread through social networks Political knowledge within socio- political networks Academically, professionally taught and diffused
Knowledge in urban governance Who are producers and users? What knowledge instruments are used in these networks? Incentives? Whose knowledge is included, excluded? What knowledge coalitions are formed? – Urban government and private developers – Civic organisation and citizens – Government and civic organisations
Knowledge in Local government Administrative knowledge used: – Planning instruments and procedures; – Funding programme requirements Knowledge excluded: – Local community knowledge, – Academic knowledge from other sources; – Own databases for trend analyses; – Remote sensing sources; Potential exists for spatial information (Tool 1)
Community perspectives on knowledge Knowledge used by communities: Experience of lived spaces (quality of life issues) Local knowledge on opportunities and limitations Knowledge on social and political relations Tacit community knowledge, social and political relations often ignored in urban management For inclusion participatory knowledge generation can provide new instruments (Tool 2)
E-governance: interaction and knowledge exchange (Tool 3) Assumptions: increase of Efficiency Revenues Accountability Transparency Reduce corruptions Learning But – Exclusionary or participatory?
Untapped potential of integrated knowledge (Tool 4) Geographical information systems – Matching thematic information to localities – Visualization of spatial patterns and trends – Overlay of different sources of information => Knowledge integration and monitoring
Approaches to spatial knowledge production Utilizing existing databases – Census,..(tool 1) Drawing in tacit knowledge by participatory methods – community, professional, CSO sources (tool 2) Analyzing e-based information (tool 3) Combining sources of information – remote sensing, mining databases, web2.0 (tool 4)
Baud, Sridharan and Pfeffer (2008), Tool 1- Using Census for mapping Hotspots of poverty in Delhi, with Multiple deprivation index
ISA,SRI & KarIn (c) 2007 Tool 1: Mumbai & Chennai Poverty hotspots
ISA,SRI & KarIn (c) 2007 Tool 1: comparing databases for analysis: Poverty in slums?
Basic and formal built-up area Informal built-up area Low densityHigh density A type B type Urban structures in Delhi (India) Baud, I, Kuffer, M, Pfeffer, K, Sliuzas, R V & Karuppannan, S 2010 Understanding heterogeneity in metropolitan India : the added value of remote sensing data for analyzing sub - standard residential areas. International journal of applied earth observation and geoinformation: JAG 12 359-74.
Combining understandings Tool 1 based on calibrating in-depth knowledge of local situations: – Households priorities in livelihood strategies – Negotiations with organisations providing for needs (housing, employment, services) – Political processes of provision (middle-class route versus poor households political channels (councilors)) – Heterogeneity in cities: gauthans, lifestyle cities (source: van Dijk, 2008,2009, 2010)
Tool 2: drawing in tacit knowledge – participatory workshops Councillors and administrators combined Setting issues priorities Setting spatial priorities Outcome: – Common understanding – Contestations
Tool 2: Prioritizing issues by ward in Kalyan -Dombivili
Tool 3. Complaints and Index Multiple deprivation Complaints not necessarily concentrated in the most deprived areas according to IMD. ( Source: Pfeffer, Martinez, Baud, Sridharan, 2010 ) Ward 24
Tool 3: Classifications and embedding Source: Richter, Miscione, De, Pfeffer, 2010 Databases based on mixed classifications Criteria and political Digitizing local databases still unevenly implemented Linkages between local, state and national government still uneven Exclusionary potential underestimated
Tool 4: Integrating different knowledge sources Example Hubli-Dharwad, SPA Master students, coordinated by N. Sridharan Fieldwork combined with Census-based deprivation mapping Consultations with local government and communities Analysis integrates different types of knowledge: conclusions fed back to local government
In conclusion Tools, claimed potentials and unnamed limitations 1 – utilizing existing databases strategically – spatialisation provides extra information on urban segregation and hotspots 2 – database classifications may be mixed, providing skewed analytical results 3 - tacit knowledge supports more inclusionary processes, provided stakeholders are not excluded 4 – e-based grievance systems may be efficient, but danger of excluding needs when deprived groups do not utilize these channels 5 – integrated knowledge base provides more balanced picture: needs feedback in policy discussions 25
In conclusion Linking tools to urban governance Network governance Hierarchical governance Knowledge governance Tool 1: analyzing existing databases with theoretical framework +++ Tool 2: tacit knowledge through participatory processes ++-+ Tool 3: mining/creating databases +++ Tool 4: integrating various sources of information and knowledge +++