Keywords

1 Introduction

The district is slowly establishing itself as the main unit in spatial planning due to the growing significance of community-based development strategies. The emphasis on the community in sustainable development practices is more than just a recognition that many low-income communities will bear the greatest cost of climate change due to vulnerabilities resulting from high levels of natural resource dependence [1, 2] or as a consequence of the inadequate provision of infrastructure in low-income urban areas [3]. It is also an acknowledgment of the frequent failure of economic growth policies based on western-style modernization and industrial growth [4, 5] in achieving sustainable community development [6]. Even during the recent COVID-19 pandemic the disproportionally greater impact on vulnerable parts of the society and the urban poor has only further exacerbated existing disparities [7, 8]. Calling into question how to objectively measure risk and vulnerabilities to have a policy commitment for a just allocation of resources to mitigate these negative impacts in the future. This prompted policy-makers to approach the discourse on “development” and consecutive policies tackling multidimensional inequalities, with greater caution. The shift towards place-based policies was a response to the limitations of previous policies promising to deliver “harmonious development” − such as the EU Cohesion policy − which instead fell prey to (i) space-ignorant one-size-fits-all approaches; (ii) passively accommodating agglomerations led by corporate decisions; and (iii) turning local elites into rentiers, rather than compensating lagging areas − only further strengthening existing inequalities [9]. The rejection of a top-down approach and working towards the utilization of indigenous strengths by harvesting the assets and capacities of the community has led to community-based and community-led development approaches becoming the fastest growing mechanism for driving development. As of June 2020, there were 327 active community-driven development projects in 90 countries promoted by the World Bank for total lending of $33 billion [10]. Neighbourhoods have thus become the centre topic of discussions on climate change and a target scale for driving urban transitions toward a sustainable and resilient future.

And while certain aspects of urban planning which have previously been suppressed by neoliberal practices, such as the quality of open public space, are now being brought back to the forefront of discussion [11]. Lockdowns and physical distancing mandates have underscored the significance of one’s vicinity to essential resources as extremely valuable. On the other hand, urban density has come under fire as a potential weakness in a pandemic situation bearing the responsibility of increasing transition rates. The question of whether dense urban morphology contributes to the rapid spread of infectious diseases has been the subject of several studies [12,13,14]. At the same time studies have put forward social-distancing models [15] as an effective [16] spatially related non-pharmaceutical intervention for mitigating and controlling primary outbreaks. However, evidence of the toll social distancing has on mental health is rapidly growing [17,18,19]. And while social distancing can help in slowing down the spread of infections, the history of pandemic-influenced urban design can offer us insight into spatially based solutions for creating an urban space less susceptible to disease outbreaks. Thus, simultaneously lessening the necessity of social alienation and minimizing the negative mental health side-effects. Historically public health is intrinsically related to the quality and design of space [20] and is an important point in sustainable and resilient urban development. Hence the pandemic introduced an opportunity to constructively build upon lessons learned from this challenging situation and shift from a reactionary character of urban planning toward planning for transition. In other words, to reshape urban areas and cities to be transformed into regenerative and liveable places addressing the climate crisis while improving the quality of urban life for all residents equally [21, 22] scaled down to the human level. Research has shown an association between the physical configuration of the street network and the distribution of social activities. However, state of the art measures for resilience focus on a macro urban scale, failing to address the human scale of the urban fabric. In other words, they overlook the role urban configuration plays in fostering sustainable practices and boosting resilience. The paper introduces a framework for driving urban transitions and downscaling urban intervention to a neighbourhood level based on a process utilizing a policy commitment- (i) the National Risk Index; a robust assessment tool for mentoring the level of a city’s vulnerability- (ii) City Resliced Index; and a methodology for providing feedback by controlling for effects on a neighbourhood level through scenario testing - (iii) Space syntax approach.

The objective of this paper is to bridge this scale divide in measuring resilience and sustainability by introducing the Space Syntax approach which quantifies the performability of urban space by correlating spatial attributes, associated with the distribution of social activities, with indicators of resilience derived from the City resilience index. Utilizing different sets of data and evidence-based tools can help develop a pandemic and climate-resilient urban strategy through a progressive planning approach. The introduction of a scenario testing practice based on the space syntax methodology can promote sustainable urban planning by allowing adjustable district borders depending on different resilience goals and their diverse area size requirements (15-min city, positive energy districts, and circular urban economies).

2 Theoretical Background

2.1 Neighbourhood-A Unit of Urban Space

As cities work towards recovery from the COVID-19 pandemic, the ‘15-min’ city concept is gaining momentum. With climate change still the focus of global concern, C40 Mayors’ Agenda for a Green and Just Recovery encouraged a wider implementation of the 15-min concept [23] promoting the concept as a panacea. While this concept is not necessarily equally suitable for all cities the increasing digitalization of services followed by an accelerated systemic shift to remote working brought on by COVID serves as the impetus to apply the principle of neighbourhood unit regardless of the city size. The 15-min city was adopted by Carlos Moreno from the ‘neighbourhood unit’ concept, introduced by Clarence A. Perry. The urban model, a neighbourhood, was designed for a population of about 5,000–9,000 residents, with a school, place of worship, and recreational area at its centre. Commercial uses were shifted to the perimeter of the neighbourhood along arterial streets which defined the boundaries of the neighbourhood. The intention was to create a unit of urban morphology in which pedestrians were able to move freely along interior curvilinear streets without interference from high-speed vehicular traffic [24]. Similar to the neighbourhood unit, the 15-min concept offers a highly functional mixed-use, socially concentrated planning paradigm − an inclusive system promising higher resilience, particularly since lockdowns have forced people to re-think their lifestyles and opt for ‘local’. The ‘15-min’ city concept is a decentralised urban planning model, in which every neighbourhood contains all the essential amenities for living and working available within a 15-min walking or cycling distance. This basic notion of a walkable mix-use unit would reduce the need for frequent long-distance travel while strengthening a sense of community, and improving sustainability. The stimulated social interaction and cohesion among residents living in the same neighbourhood underpin the assumptions within urban planning that are associated with permeability, mixed uses, and high densities promoted by Jane Jacobs in 1961. Her concept ‘eyes on the street’ promotes not only vibrancy, via a diverse mix of land-uses, but more natural surveillance and the potential to reduce opportunities for crime [25]. In design terms, this means that residents potentially have increased opportunities to ‘self-police’ the streets in certain housing layouts, which are configured to face each other and the street. The underlying premise that spatial density and permeability facilitate social cohesion and cooperation will be further elaborated through case studies based on the space syntax approach.

2.2 Space Syntax Methodology and Space Sustainability

Space syntax method and theory enable us to describe the spatial properties of a sustainable city, and through an operational method analyse spatial relationships between built objects. It has introduced refined knowledge about the relationship between space and society [26]. The space syntax method analyses street networks and can shed light on the walkability qualities of a researched area or neighbourhood. Above walkability, space syntax can offer additional information on the spatial qualities relating to spatial resilience and sustainability.

Space syntax encompasses both a theory and a method constituted by a collection of analytical tools used to study urban morphology and uncover the logic of space that affects human behaviour [27]. Space syntax is based on a philosophy of design that puts forward architecture and urban design, in both spatial and plastic sense, as fundamentally configurational [28]. The theory argues that the universal statistical behaviour of space syntax measures uncovers the universality of the human-urban interaction mechanism and that the configuration of space itself is the main generator of movement [29] yielding attraction and privileging certain urban spaces over others regardless of land use.

The space syntax method quantifies the spatial network using axial maps presented by the least and longest set of axial lines (lines of sight) covering all open spaces in an urban environment [30]. These axial lines form a basis for network configuration analysis. Apart from axial maps, segment maps are used to perform segment-based analysis when small-scale research is needed. Segment maps are derived from axial maps in which axial lines are split at intersections to form segments [31]. Visibility graphs are derived from isovists-the area of space directly visible from a viewpoint located within the same space [32]. Visibility graphs are generated by superimposing a grid on a vector drawing of a space (e.g. a CAD drawing) using space syntax software. Syntactic measures can then be computed for each grid cell using space syntax measures for quantifying the proximity and inter-visibility of grid cells [33].

One of the basic ideas in measuring spatial relations is the concept of depth, meaning the distance between any pair of spatial elements. Three definitions of distance are used: (i) Topological distance-the number of turns from one space to another; (ii) angular distance-the angular change from one space to another; (iii) metric distance-the Euclidean distance in meters from one space to another. Different spatial patterns will be generated by utilizing the three types of distance. The axial representation is utilized to derive topological distance, whereas the segment map utilizes all three distance measures. However, all three concepts of distance can be applied to calculate different syntactic measures.

Integration (or Closeness).

One of the basic syntactic measures is integration or mathematical closeness. Integration illustrates how close or how accessible each spatial element is to all others under each definition of distance, such as the least angular distance [28, 31].

Choice (or Betweenness).

Another frequently used syntactic measure is choice or mathematical betweenness. Choice measures the degree to which each spatial element lies on the shortest paths, under each definition of distance, between any pair of spatial elements [31, 34, 35].

The difference between these two measures is that choice assesses the potential of the movements passing through each space (through-movement potential) whereas the to-movement potential is measured by integration. Therefore, through-movement patterns can be examined by producing a choice pattern.

Some of the other syntactic measures used are connectivity-number of spaces immediately connecting to a space of origin [28, 30]; total depth-the sum of the topological depth from any node to all the others [34]; centrality-a measure that quantifies the probability that a street segment will fall on a randomly selected shortest path linking any other pair of segments [31, 36]. The measures most commonly used in space syntax are angular choice and angular integration at various metric radii utilizing axial and segment visual maps, because these measures have been supported and verified by a large number of studies and practical applications [31]. Visibility integration is the most frequently used measure in visibility graph analysis [33].

To operationalize boundaries and gain understanding as to why the three different distance concepts yield different patterns the concept of Radius was introduced as a tool for selecting sub-systems. Topological radius is defined by a fixed topological depth from any given origin (e.g. 4 directional changes). On the other hand, the metric and angular radii refer to all aspects within a fixed metric distance from an origin (e.g. 5 m). In addition, angular distance can also be calculated by angular step. Generally, urban street networks that bring about movement and display higher visibility, encourage street-level use intensification over a period of time. A city’s spatial form and configuration influence how people move − public transport, cars, bicycle, or on foot. An initial rapid scope literature review has uncovered various connections between syntactic measures and different social activities. Urban morphology has shown to correlate with the placement of (i) economic activities [37,38,39,40]; (ii) crime rates [30, 41,42,43,44], and (iii) research collaboration and exchange of ideas [45, 46]. This can be explained by the frequency of pedestrian movement – the more spatially integrated a street segment is, the higher the flow of pedestrians and the more the space along that street segment will attract economic and other activities [38], and the opposite the less an area is integrated the higher the segregation will be and therefore aggravate urban exclusion from essential services like education and health services [47]. Urban interventions, such as street links have been shown to influence this process [48]. This theory shows the authority a street network’s spatial configuration has over movement, and subsequently social and economic activities [49].

2.3 Measuring Resilience

The question of how to measure vulnerabilities and resilience has resulted in several methodologies and indexes which aimed to assess the level and type of risk for governments to properly allocate resources where needed and help boost community resilience. Here two types of indexes will be discussed in detail: ARUP City Resilience Index which utilises both qualitative and quantitative data, and the FEMA National Risk Index which utilizes quantitative data.

The concept of resilience is not in conflict with sustainable urban policies [50] and therefore should be viewed as complementary approaches in urban planning − to design for true sustainability is to design for resilience. Resilience is the ability of a system to respond and recover from a shock quickly, while sustainability imposes the imperative of continuance. A sustainable model is usually defined as that which balances economic, environmental, and social issues equally well, but it may not by default accommodate change efficiently or recover quickly after a shock. Therefore, it is important to underline that while sustainability encompasses the three aforementioned categories, resilience is derived from the quality of their interlinkages and introduces new imperatives: (i) reflection-having monitoring mechanisms put in place in order to adequately adjust the system according to new altered conditions; (ii) robustness-implement physical assets which are in terms of construction and management conceived in a way to withstand impacts of hazards without a great loss of function; (iii) redundancy-the spare capacity porously created with an aim to accommodate disruption by mitigating surges in demand; (iv) flexibility-the capacity of a system to change, evolve and adapt in response to changing circumstances; (v) resourcefulness-the the ability of people and institutions to anticipate future conditions, set priorities and secure a system of coordinated response by mobilising wider human, financial, and physical resources; (vi) inclusivity-need for broad engagement and participation among communities, including the most vulnerable groups; (vii) integration-alignment and integration among city systems to promote consistency in decision making to ensure all investments are in synergy and supportive of common goals [51]. However, the efforts to develop more resilient and sustainable systems revealed the need for a methodology that would be able to develop a baseline relative risk measurement by leveraging available source data and community vulnerability factors. A comprehensive index that could measure the relative vulnerability of a community, or a city, could help inform planners, emergency managers, and the general public, but most importantly-policymakers and support their decisions regarding priority and allocation of resources, updating emergency operation plans, enhancing hazard mitigation plans, identify a need for a more detailed and refined risk assessment for specific areas, inform and educate residents and homeowners, encourage community-level cooperation and engagement, enhance and update codes and standards, and set up long-term community recovery. There is a plethora of literature dedicated to framing and understanding vulnerability [52,53,54,55,56,57]. Still, definitional discrepancies persist due to a myriad of climate change research and the diverse scientific communities involved [58]. Though the broad framework considers vulnerability as a function of sensitivity, exposure, and adaptability [59] it is also seen as a synthetizing concept that links the biophysical and social dimensions of climate change [60, 61].

National Risk Index (NRI).

Before the National Risk Index, a plethora of tools and methods to assess risk have already been developed and utilized by a variety of different agencies across the US. The 2012 Disaster Resilience report by the Nation Research Council USA outlined 17 assessment tools and systems, and since the report was released there has been an explosion in the number of additional resilience measuring tools developed by government agencies, academia, NGOs, communities, and the private sector. These tools vary in range and purpose—quantitative to qualitative, bottom-up to top-down, hazard-specific to hazard-neutral, local to global, and pre- to post-event [62,63,64]. Beginning in 2016, FEMA’s NHRAP (Natural Hazards Risk Assessment Program) started work on the National Risk Index by adopting an informed vision for a multi-hazard view of risk that combines the consequence and likelihood of natural hazards with resilience capabilities and social factors. The goal was to form a holistic, comprehensive view and create a nationwide baseline of natural hazard risk.

The NRI’s purpose is to decrease human and infrastructure costs through smart pre-disaster investments. Studies have consistently shown FEMA hazard mitigation investments are cost-effective and result in communities that are safer and more resilient to natural hazards, including impacts from climate change. An analysis of the “American Recovery and Reinvestment Act showed that investments in community resilience generated 15 to 33 jobs per million dollars spent and an economic return of $2.40 for every $1 invested” [65]. Therefore, the National Risk Index promotes a systematic risk evaluation which can inform policy-makers of the most rational investments and resource allocation strategies [66]. In 2021 FEMA released a National Risk Index (NRI) as a baseline risk assessment application resulting from a collaboration with a variety of federal, state, and local governments, academic institutions, non-profit groups, and private industries. All data supporting NRI components were required to be nationwide in scope and able to be measured at the Census tract level. Hazard type data sources were also required to have location and time information. However, some data that met these requirements were simply not available for the Census tract level, unlike the County level-which is complete. For Example, Social Vulnerability scores are available for all counties but are absent for 292 Census tracts. Calculations are performed separately at two levels—County and Census tract—so scores are relative only within their level. The committee for NRI reviewed the strengths and weaknesses of different frameworks for measuring resilience, and identified four critical dimensions of a consistent system of resilience measures or indicators [64]:

  1. 1.

    Vulnerable Populations—factors that capture the needs of individuals and groups in relation to indicators such as minority status, health issues, mobility, and socioeconomic status

  2. 2.

    Environmental Infrastructure—the ability of infrastructure to recover from critical events—these include water and sewage, power, transportation, communications, and natural infrastructure

  3. 3.

    Social Factors—factors that enhance or limit a community’s ability to recover, including elements such as social capital, language, education, financial structures, culture, and the quality of the workforce

  4. 4.

    Built Infrastructure—the ability of built infrastructure to withstand impacts of disasters, including elements such as public services, homes and businesses, bridges, and roads

The general National Index Risk Eq. (1) is as follows:

$${\text{Risk}} = \left( {{\text{Expected}}\;{\text{Annual}}\;{\text{Loss}} \times {\text{Social}}\;{\text{Vulnerability}}} \right)/({\text{Community}}\;{\text{Resilience}})$$
(1)

Social Vulnerability.

The social vulnerability source data is provided by the University of South Carolina's Hazards and Vulnerability Research Institute (HVRI) Social Vulnerability Index (SoVI). SoVI is a location-specific assessment of social vulnerability that utilizes 29 socioeconomic variables, which the literature research suggests contribute to a community’s reduced ability to prepare for, respond to, and recover from hazards [67].

Community Resilience.

Community Resilience is defined by the National Institute of Standards and Technology (NIST) as a community’s ability to prepare for anticipated natural hazards, adapt to altering conditions, and recover rapidly from disruptions [68].

The community resilience index was provided by the University of South Carolina’s Hazards and Vulnerability Research Institute (HVRI) and is adapted from their Baseline Resilience Indicators for Communities (BRIC) index [69]. The community resilience score is a consequence reduction risk factor and represents the relative level of the community’s resilience relative to all other communities at the same level. Consequently, a higher Community Resilience score results in a lower Risk Index score. Because Community Resilience is unique to a geographic location (county) it is a geographic risk factor. Since there are multiple ways to define resilience at the local level but no nationally available, bottom-up community resilience indices are available, and the key drivers of resilience vary between locations, the Social Vulnerability and Community Resilience chose to utilize a top-down approach. The source data is provided by the University of South Carolina’s Hazards and Vulnerability Research Institute HVRI) and is only available at the county level. Therefore, Community Resilience scores were inferred from counties to Census tracts by assigning each Census tract the score of its parent county.

Expected Annual Loss (EAL).

The EAL for each county or Census tract is the average economic loss in dollars resulting from natural hazards per year. EAL is computed for each hazard type and only quantifies loss for specific consequence types (i.e. buildings, population, or agriculture). The historic losses source data are provided by the Spatial Hazard Events and Losses Database of the United States (SHELDUS) Arizona State University’s (ASU) Spatial Hazard Events and Losses Database. EAL is estimated for 18 hazard types: avalanche, coastal flooding, cold wave, drought, earthquake, hail, heat wave, hurricane, ice storm, landslide, lightning, riverine flooding, strong wind, tornado, tsunami, volcanic activity, wildfire, and winter weather.

The equation for Expected Annual Loss (2) is as follows:

$${\text{Expected}}\;{\text{Annual}}\;{\text{Loss}} = {\text{Exposure}} \times {\text{Annualized}}\;{\text{Frequency}} \times {\text{Historic}}\;{\text{Loss}}\;{\text{Ratio}}$$
(2)

City Resilience Index (CRI).

The City Resilience Index developed by Arup [51] is perhaps the most comprehensive holistic framework taking into account the economic, physical, and social disruption; and that is applicable at the city scale rather than to any individual system within a city. The index has been designed to enable cities to assess and monitor the multiple factors which contribute to their resilience. The Index provides a means to assess and measure the extent to which the city is achieving the 12 goals, based on 52 indicators according to the 4 dimensions (see Table 1). The aim of the City Resilience index is also to focus on assessing the performance of a system before a hazardous event, rather than to mitigate or prevent the loss of assets due to a specific event. It utilizes both a quantitative (when data is available) and a qualitative approach.

Table 1. City Resilience Index, ARUP [51]
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Fig. 1.
figure 1

Conceptual map of research methodology

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The CRI and NRI both reveal vulnerabilities and show the extent of an urban system’s ability to respond to a disaster. They are confined within available data and promote analysis on a macro urban scale. The greatest difference is that NRI is US specific, in that it is calibrated according to data sets available for the US. The CRI is formed as a comprehensive basis of measurement based on research that revealed universal factors deemed to contribute to city resilience − although their relative importance varies according to contextual specificities. CRI was developed with the intent to be both relevant and accessible to cities globally regardless of their size, capacity, location, or availability of data-which is why its value lies in the addition of qualitative methods for data acquirement. However, both indexes overlook the role urban space plays in boosting resilience by way of spatial configuration. To scale down and facilitate effective implementation of the 15-min the proposed framework is enforced by the addition of a qualitative method that can examine the built environment and locate vulnerabilities on an urban fabric level − Space Syntax.

3 Research Design and Methods

This proposed framework (see Fig. 1) for driving urban transitions towards a sustainable and resilient future puts forward a multiscale framework based on an assessment of vulnerabilities through the quantitative tool (i) the National Risk Index-which dictates policy commitments; monitored through a holistic, more comprehensive, qualitative and quantitative (ii) City Resilience Index; while controlling for effects on a district scale through a set of analytical techniques for evaluating the syntactic properties of urban models with (iii) the space syntax method. The paper employs a structured literature review of space syntax publications in order to (i) detect; (ii) summarise; and (iii) reveal those studies that statistically correlate at least one syntactic measure with indicators based on the twelve goals from the City Resilience Index. The utilization of CRI is justified by its universality of indicators applicable to a global context. The hypothesis is that space syntax measures can serve as indicators for the aforementioned goals and enable or restrict the policy and subsequent investment from producing the desired outcome. To do so the paper sets out eligibility criteria to assess the eligibility of reported studies (referred to as primary studies). The included primary studies: (i) applied at least one of the two representations to conduct space syntax analysis: axial or segment; (ii) reported a quantitative association between any space syntax measure of spatial attribute and topics deducted from the twelve goals of the City Resilience Index; and (iii) presented valid regression model or correlation analysis; (iv) included primary data (not based on simulation); (v) reported findings in English; and (vi) have a study area at the level neighbourhood or district.

3.1 Search Method for Deducting Relevant Studies

The systematic analysis was conducted according to a four-step process outlined below: First, comprehensive database searches were conducted within all major electronic publishing databases including SAGE (n = 686), Science Direct (n = 895), Tailor & Francis (n = 601), MDPI (n = 2), JSTOR (n = 108), Space Syntax Network (n = 7), SPRINGER Link (n = 11,437), and Wiley online library (n = 163). The following research strings were modelled according to the formula: “methodology” AND (objective) AND (indicator variable) AND (syntactic representation) AND (relevant connection). Because certain search engines do not support wildcards they were excluded from search strings. Appropriate indicator variables for target objectives were derived from the first phase of the space syntax literature review. Only those found to be correlated with indicators listed in the City Resilience Index were chosen. For example, for the goal “minimal human vulnerability” the indicator “1.1 Safe and affordable housing”: for safe residential areas, the indicator for safety is “burglary incidences” which are correlated with specific spatial layouts, and is, therefore, a syntactically measurable effect. The following research strings were used: “space syntax” AND (house OR residential) AND (crime OR burglary) AND (axial OR segment OR visibility) AND (correlation OR regression); “space syntax” AND (economy OR retail) AND (pedestrian OR mobility) AND (axial OR segment OR visibility) AND (correlation OR regression); “space syntax” AND (health OR well-being) AND (medical OR services) AND (axial OR segment OR visibility) AND (correlation OR regression); “space syntax” AND (community OR vitality) AND (pedestrian OR walkability) AND (axial OR segment OR visibility) AND (correlation OR regression); “space syntax” AND (policing OR crime) AND (sex OR drug) AND (axial OR segment OR visibility) AND (correlation OR regression); “space syntax” AND (infrastructure OR “land use”) AND (segregation OR consolidation) AND (axial OR segment OR visibility) AND (correlation OR regression). Because Science Directs limits the number of Boolean operators to eight the search strings were repeated separately with ‘axial’ and ‘segment’ graph, and later with ‘visibility’ graph.

Fig. 2.
figure 2

Methodology for selection of primary studies

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The above search process yielded 13,899 documents which were examined systematically as depicted in Fig. 2. The titles were initially checked for relevance and duplication which screened out 13,009 documents. The remaining studies were further screened based on title and abstract to further exclude those which failed to provide information that would render them relevant for this study. Based on this 621 additional document was excluded. The full text of the remaining 269 studies was examined for data, and 233 were eliminated based on insufficient data or mismatched data. Leaving 36 studies to be included and cross-referenced with indicators from the CRI and divided according to the goals (see Table 2).

Table 2. Space syntax case studies utilizing syntactic measures found to be correlated to the 12 goals of CRI
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3.2 Scenario Planning

Space syntax method and theory enable us to describe and evaluate the spatial properties of a sustainable city. A posteriori reveals the relationships between a built environment’s spatial attributes and socio-economic patterns occurring within it. However, in line with the new tendency of pro-active urban planning Space syntax reveals itself as a valuable tool in that sense as well. In the a priori case, it can be applied as a tool in urban analytical design for scenario development and option testing in all phases of urban intervention to evaluate the spatial effects of various urban design proposals and how they can intensify human activities and movement. Thus space syntax can support a decision-making process for a well-functioning urban design, allowing the development of sustainable cities and communities.

Space syntax can offer an answer to the ‘what if’ question. A case study examining the likely effects of two different locations for a new bridge across the Nieuwe Maas River in Rotterdam illustrates the scenario testing capability of the space syntax approach. This scenario testing was originally conducted in 2012 for the municipality of Rotterdam [97] to improve the spatial integration of the southern part of Rotterdam. One bridge placement option indicated an increase in the spatial integration of the southern centre of Rotterdam. In doing so, the centre has the spatial potential to become the main centre of Rotterdam. The other indicates a shift of the main centre of Rotterdam towards the western part of Rotterdam south. The conclusions from this case study are rooted in the theory of the natural movement of economic process [49], however, it does not attempt to test and evaluate results according to spatial attributes found to be correlated with indicators for resilience.

For this reason, the methodology design is based on systematic analysis. The reproducibility stemming from the method design allows for further testing and additional case studies, and subsequently, an opportunity to refine the application.

4 Discussions and Conclusion

The notion of urban transition towards sustainability and resilience supported by various world organisations [23, 98, 99] promotes a holistic, comprehensive approach, one which encompasses land-use planning and spatial organisation at various scales per the current needs of societies and includes actions aimed at improving living conditions, stressing mobility, accessibility, and greening [21]. The overwhelming effect of COVID-19 has recently steered the discourse towards a human scale and a people-centred approach. Promoting the importance of the hyper-local environment [23] to support quality of life by adopting a 15-min city strategy means introducing an urban model that allows everyone, in every neighbourhood, to meet most of their daily needs within a short walk or bike ride from their home. However, the materialisation of policies and learned behaviour of people can be a make-or-break point in delivering desirable results. Therefore, the identification and analysis of factors that encourage specific patterns of behaviour concerning space as well as the resulting economic and social consequences: in other words, that which emerges from the interrelation between the built environment and its users; can be crucial in achieving urban transitions. The ‘spatial turn’, conceived by Lefebvre [100], wherein space is not the ‘container’ of events generated by people but a fully-fledged actor, actively influencing the societies functioning opened the door for socio-spatial dialectics, wherein the ‘spatial’ shapes the ‘social’ as much as the ‘social’ shapes the ‘spatial’ [101]. Spatial logic would seem to hold insight into why policy outcomes are enabled or restricted to perform efficiently and concerning conflicting groups of actors. In other words, space shifts the emphasis in favour of certain policy perspectives and thus reveals the bias of the public agency. Thereby stressing the need for a spatially analytical method that can quantify spatial attributes in correlation to social activity patterns and render adequate urban design solutions aligned with spatial planning strategies and subsequent urban policies.

The literature review has demonstrated that various spatial patterns can generate different outcomes in terms of economic and social activities, affecting residents’ well-being, cooperation, sense of belonging to a community and perceived urban safety. The introduced cased studies based on the space syntax methodology may support this notion by establishing significant correlations between spatial attributes and real-world and historical data.

Therefore, this space syntax methodology can be an effective tool in helping policymakers and researchers evaluate favourable urban policies, strategies, and control for effects during the designing process of future developments and urban interventions with scenario testing. A top-down framework consisted of (i) the National Risk Index to locate vulnerabilities and assess the appropriate resource allocation and investment commitments; a broad comprehensive and robust (ii) City Resilience Index to diagnose the strengths and weaknesses of a community and monitor relative performance over time; and (iii) space syntax method as a scenario testing tool as a control-method used to render the most appropriate design solution in line with policy aims can help drive urban transitions.

A conceptual framework based on a top-down approach focused on the human scale would provide additional support to governmental policies in not only disease prevention and control, but promote sustainability practices complementary to resilience capacity building. Analysing and detecting vulnerabilities as the first step would provide an aim and targeted area for resource allocation. The next step of scaling down to a human scale would help resolve detected vulnerabilities on an urban texture level, and thereafter introduce a plan which will provide residents with all essential needs in a 15-min radius. Tailored strategies and urban design solutions are needed to provide an upgraded urban space and green infrastructure. Since not all solutions will provide the desired result, scenario testing of proposals should be conducted to render the most spatially sustainable and effective solution.