Speaker
Description
Keywords: relational planning; digital twins; uncertainty; municipal governance; sense-ability
Introduction and research problem
Cities are increasingly understood through data. Digital twins, artificial intelligence, remote sensing and predictive analytics promise more complete representations of urban reality, offering planners the possibility of anticipating change before it becomes visible on the ground. Much of this work assumes that uncertainty is a technical limitation that can be progressively reduced through better models, more comprehensive datasets and increasingly sophisticated computational methods.
This paper begins from a different proposition. Uncertainty is not simply the absence of knowledge. It is a constitutive condition of planning in complex urban systems. Cities continually become through relationships between people, institutions, infrastructures, ecologies and technologies. Their reality is therefore never fully contained within any single representation, however accurate the underlying data may be.
The research asks a question that lies at the heart of the AESOP Young Academics conference: Whose knowledge counts under uncertainty? Rather than contrasting expert knowledge with citizen knowledge, or digital evidence with lived experience, the paper argues that legitimate planning emerges through the continual relationship between multiple forms of knowing. Digital twins become valuable not because they eliminate ambiguity, but because they provide shared representations through which diverse forms of knowledge can become mutually intelligible and democratically answerable.
The empirical context is the City of Cape Town, where municipal valuation, geospatial information and constitutional governance intersect within a legally regulated property system. The case provides an opportunity to examine how digital representations influence evidence production, institutional legitimacy and decision making while remaining grounded in everyday municipal practice rather than experimental laboratory conditions.
Conceptual and theoretical framework
The paper introduces sense-ability as a relational capacity for planning under uncertainty. Sense-ability refers to the collective ability of institutions and communities to perceive, interpret and respond to emerging urban change through shared representations. It differs fundamentally from data accumulation. More information does not necessarily produce greater understanding. Instead, understanding emerges through the relationships established between representations, professional judgement, citizen experience and institutional action.
This distinction leads to a broader conceptual separation between institutional memory and relational sense-ability.
Institutional memory consists of the accumulated records through which municipalities remember their cities. These include cadastral parcels, valuation rolls, ownership histories, aerial photography, LiDAR, zoning information and long-term geospatial archives. Such records provide continuity across decades and make legally defensible planning possible. They function as the city's administrative memory.
Relational sense-ability describes something different. It is the capacity to notice what is becoming visible through changing relationships. An informal settlement extending across a river corridor, incremental roof additions, expanding tree canopy or declining vegetation are not merely data points. They become meaningful through interpretation, dialogue and institutional response. Sense-ability therefore belongs neither to algorithms nor to individuals. It emerges between them.
The framework develops four interconnected capacities for adaptive planning.
• Sense-ability is the capacity to recognise emerging conditions through shared observation.
• Response-ability is the capacity of institutions to respond proportionately and adaptively. It refers to the ability to respond rather than the obligation of responsibility.
• Predict-ability is the capacity to anticipate future conditions through iterative learning rather than deterministic forecasting.
• Answerability is the democratic condition through which planning decisions remain transparent, contestable and open to explanation.
Together these capacities reframe the purpose of digital twins. Rather than becoming instruments of optimisation alone, they become civic infrastructures supporting institutional learning, ethical deliberation and adaptive governance.
The framework also challenges the conventional language of trade-offs. Traditional planning frequently frames sustainability as the optimization of competing objectives such as development versus environment, density versus amenity or mobility versus emissions. This paper proposes a relational alternative in which interaction transforms the configuration of the system itself. The emphasis shifts from optimization toward sufficiency, understood not as minimization but as maintaining the conditions under which human and ecological relationships remain capable of continual regeneration.
Methods
The research adopts an embedded action research methodology within the City of Cape Town's municipal valuation and geospatial environment. The author occupies a dual position as practitioner and researcher, enabling the investigation of how standards, workflows and digital infrastructures co-evolve within everyday municipal governance.
The empirical material combines multiple sources of institutional evidence, including statutory property registers, general valuation data, high resolution aerial imagery, LiDAR, temporal change detection, municipal quality management processes and citizen interactions arising through objections, inspections and valuation appeals. Rather than evaluating a completed digital twin, the research documents the progressive emergence of relational governance alongside digital transformation.
Artificial intelligence is employed primarily as an observational technology. Machine learning assists in identifying roof extensions, informal settlement growth, tree canopy change and other forms of physical transformation across the metropolitan landscape. Importantly, these detections are never treated as autonomous decisions. Every identified change requires interpretation, verification and professional judgement before entering statutory governance processes.
This methodological position deliberately resists technological determinism. The digital twin is understood as an evolving socio-technical practice in which computational representations reorganise institutional attention rather than replace human expertise. The research therefore investigates the relationships between models, evidence and governance instead of evaluating algorithmic performance alone.
Results and expected results
First, institutional memory significantly improves the municipality's ability to distinguish meaningful change from ordinary urban variation. Longitudinal aerial archives reveal not only new development but also continuity. Confirming the absence of change becomes as valuable as detecting change because it strengthens confidence in valuation accuracy and resource allocation.
Second, AI assisted observation appears to widen collective sensing rather than narrow it. Instead of producing final answers, automated detection functions as an invitation to collaborative interpretation. Valuers, GIS specialists, planners, environmental practitioners and field inspectors begin from a common visual representation while contributing distinct professional understandings. The shared image becomes a relational object around which planning knowledge is negotiated.
Third, the municipal property system demonstrates that prediction is inseparable from governance. Revenue forecasting, valuation modelling and spatial simulation derive legitimacy not simply from statistical performance but from their capacity to remain answerable through legally prescribed objection, appeal and review processes. Predictive capacity therefore depends upon institutional trust as much as computational accuracy.
The anticipated contribution extends beyond Cape Town. The paper proposes that relational digital twins provide a transferable planning methodology capable of supporting adaptive governance across diverse urban contexts without assuming that cities can ever be fully represented through data alone.
Discussion and conclusion
The central contribution of this paper is the proposition that planning knowledge under uncertainty is fundamentally polyphonic. Model outputs, institutional records, professional expertise and citizen knowledge each reveal different dimensions of urban reality. None is sufficient independently, and none should occupy the position of absolute authority.
This position also requires acknowledging the limitations of both participation and digital twins. Participatory mapping has frequently increased the visibility of informal settlements and strengthened claims for political recognition. Yet participation alone does not guarantee meaningful redistribution of planning power. Community generated data may improve reliability and local ownership while remaining constrained by unequal institutions, superficial engagement and limited policy uptake. Participation should therefore be understood as a relational process rather than a procedural endpoint.
Similarly, contemporary digital twins often remain fragmented, vendor driven and disproportionately focused on technical performance. Sophisticated spatial models can identify buildings, roads and vegetation with remarkable precision, but they cannot directly perceive vulnerability, care, belonging or lived experience. These dimensions remain accessible only through relationships between people and institutions. A technically complete model may therefore remain socially incomplete.
The stakes of planning under uncertainty are epistemological as well as technological. The question is not whether digital twins produce better evidence, but whether they widen or narrow the conditions through which legitimate knowledge can emerge. A digital twin that excludes informal settlements, overlooks citizen knowledge or recentralises authority risks reproducing historical patterns of exclusion beneath the appearance of technical neutrality. Conversely, a relational digital twin strengthens democratic legitimacy by creating shared representations through which different forms of knowledge remain mutually answerable.
The paper concludes that uncertainty should not be understood as a problem awaiting elimination. It is the condition within which adaptive planning becomes possible. Digital twins are most defensible when they expand collective sensing, preserve human judgement and remain answerable to democratic governance. Their value lies not in producing certainty but in strengthening the relationships through which cities continuously become knowable.
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