Unveiling Urban Void Potential

The development of an Urban Void Analysis Index (UVAI) for city section revival: A case study of Akurdi, PCMC, India

Abstract

Rapid urbanization, fragmented planning processes, unplanned peri-urban areas, inter-authoritative issues, erratic funding allocation, land speculation etc. posed serious challenges that substantially caused the generation of urban voids. Extensively, the unscientific, biased, and reactionary decision-making has instigated the need for a multi-layered assessment of urban voids. Several researchers addressed these concerns; however, they were limited in their qualitative understanding and inadequate to provide prioritization of urban voids for development. Hence, to address the challenging concern of decision-making, the present study put forth a scrupulous city section (zone) level hybrid investigation of urban voids by considering the Akurdi zone in PCMC (Pimpri-Chinchwad Municipal Corporation), India as a case. The groundwork recognized the imperative need for an ‘Inventory’ aiding the holistic and deeper assessment of urban voids. Further, using this inventory as a foundation, a multi-layered analysis was performed utilizing primary survey techniques. This unravelled the inherent development potential of urban voids in the form of an ‘Urban Void Analysis Index’ (UVAI) leading to their prioritization. This index has been derived by employing the hybrid MCDM methods mainly the AHP for weightage combined individually with TOPSIS, MOORA, and VIKOR methods. Ultimately, the comparative correlation of all hybrid methods yielded uniform prioritization results proving the robustness of the methodology. Cumulatively, this unbiased, scientific, and structured framework can guide decision-makers in an appropriate allocation of available resources with reliability and precision aiding in reviving such valuable public land assets.

Introduction

About 50% of the world’s population will be residing in urban areas by 2050, states the United Nations (UN Habitat, 2022). Across the world, cities are experiencing dramatic growth due to rapid population increase as well as technological and political transformations in the global economy (Cohen, 2006). Significantly, these changes have a visible impact on the sustainability of the densely populated and fast-developing South Asian cities (Kushwaha, Nangia et al., 2023; Mitra, AP and Sharma, 2012). This surge in the economy created growing opportunities in urban areas alluring the widespread influx of migrants from rural hinterlands to all tiers of cities (Mitra, Arup and Murayama, 2009; Nandi and Gamkhar, 2013). Such migration has been more rapid than projected, creating alarming transformations in economic structure, environment, sociocultural life, and prominently urban land management and shifts in land uses (Mondal and Banerjee, 2021; Sharma, S., Saini et al., 2023; Shaw, 2005).

India is one of the fastest-growing economies, has been facing similar challenges. The speedy urban growth of Indian cities is outstripping their capacity to provide suitable urban lands with adequate services (Jain, Korzhenevych et al., 2019). Further, the urban land is a fundamental entity required to execute mainly the macro and micro-scaled public infrastructure projects (Dhote, Silakri et al., 2013; Praharaj, 2021). However, due to the scarcity of land, macro and micro-scaled urban renewal has become a continual affair in these cities (Raisoni, H, Singh et al., 2019). Out of this, several macro-scaled urban renewal projects have been futile as they adopted an irrational and clean-slate approach which hardly has a deeper connection with the actual context (Maringanti, 2016; Raisoni, H, Singh et al., 2019). Hence, it demanded the need for several micro-scaled initiatives which not only can revive these lands but also possess the potential to revive the urban fabric.

In many cases, public agencies attempted to consume a significant proportion of urban lands for the envisaged societal purpose but failed to utilize them to their fullest capacity due to various financial and administrative reasons (Hwang, S. W. and Lee, 2020; Raisoni, Harshad, Verma et al., 2023). Predominantly, the fragmented decisions of these agencies compelled the community to exploit the land in their way creating multiple issues of unplanned lands (Sadashivam and Tabassu, 2016). Cumulatively, several such lands that remained vacant and underutilized severely affecting the urban realm are termed as ‘Urban Voids’.

These voids created multi-faceted impacts on the urban realm, therefore, the review of the relevant literature demanded the need for its multi-layered analysis (Hashem, Wahba et al., 2022). Several researchers have addressed the challenges posed by the urban voids, however, they were limited in their qualitative understanding and lacked comprehensive study with multi-dimensional parameters (Raisoni, Harshad, Verma et al., 2024). They were inadequate to provide a robust prioritization model for the development of voids and failed to address challenging situations of administrative decision-making (Aijaz, 2008; Rakodi, 2003).

Ultimately, the study raised two key concerns derived through a detailed literature review, namely the lack of ‘Methodical Framework’ and the deficiency of the ‘Prioritization Model’ in the process of micro-revitalization of Urban Voids. Hence, it attempted to create a structured process for zone-level assessment of voids using a holistic ‘Inventory Framework’ and ‘Hybrid MCDM Model’. Cumulatively, the study aims to provide a novel, unbiased, and scientific approach that can guide decision-makers in the logical selection of voids for development as well as the effective distribution of confined valuable resources meeting the demands of citizens with precision and reliability.

Literature Review

Worldwide, the continuously growing urbanization has impacted the ecology and economy of several cities especially the highly dense cities of South Asia (Kushwaha, Nangia et al., 2023; Sharma, S., Saini et al., 2023). Higher migration rates have drastically increased the population density constantly pushing the city boundary beyond Urban Local Body (ULB) limits (Mondal and Banerjee, 2021; Shaw, 2005). Even, the pace of the effective implementation of Regional, City level and Zone level plans has been significantly lower than the envisaged idea (Kamath, 1994; Minocha, 1983). Subsequently, several cities especially in India are facing challenges in planning the development of these situations arose due to rampant urbanization (Nandi and Gamkhar, 2013).

Additionally, urbanization is continuously linked to the destruction of old sociocultural spaces and green open spaces resulting in the diminishing of social character, community ethos, cultural settings and overall imageability of the city (Hashem, Wahba et al., 2022; Raisoni, Harshad and Rajaullah, 2018). The cities also observed multiple authorities making independent decisions without cohesion (Aijaz, 2008; Rakodi, 2003). Such fragmented and inconsistent decision-making has resulted in a lack of comprehensive and long-term planning thus, forcing the community to develop the precious urban land in a random and uncontrolled way (Aleha, Zahra et al., 2023; Sadashivam and Tabassu, 2016).

Consequently, these fragmented land use planning decisions led to the formulation of unconventional, ill-shaped, odd-sized, and mushroomed land parcels termed as ‘Urban Voids’ (Bhaskaran, 2018) which added difficulty, especially in reserving a proportionate number of lands for ‘Public Purpose’. In addition, these voids created several multi-layered impacts on the urban realm, hence, it was pertinent to introspect reasons for the formulation of urban voids and challenges put forth by it.

First, the most prominent reasons for void formulation have been the inter-authoritative conflicts due to poor coordination, lack of impartial decision-making, faulty planning, imbalanced fund allocation, non-accountability, and political interference (Ahluwalia, Kanbur et al., 2014; Aijaz, 2008; Rakodi, 2003). Secondly, the top-down approach of planning in the Indian context starting from the preparation of various level plans, its gazetting and its implementation has made the process very lengthy and cumbersome (Pethe, Nallathiga et al., 2014). Such delays are unlikely to compel the different authorities to make their own arbitrary decisions without considering the overall vision of the city thus adding more disputes in their policies and rights (Aruninta, 2005), in turn, creating more urban voids. Thirdly, the physio-spatial conditions of undeveloped or underdeveloped void lands have severely impacted the socio-economic and environmental aspects in and around the plots. The poor imageability and informal usage by the community also added multiple challenges in addressing the urban voids (Trancik, 1991).

Fourthly, the change in the policies and byelaws have encouraged additional floor space index (FSI) adding more population in the same urban space without a proportionate increase in public reservations (Ibraeva, Correia et al., 2020; Kamath, 1994). It has put constant pressure on the other developed reservations and overburdened the allied infrastructure creating unbalanced consumption patterns (Chakrabarti, 2001). Lastly, the urban voids are susceptible to illegal encroachments and slum formulation worsening the void situation (Hindman, Lu-Hill et al., 2015; Mahabir, Crooks et al., 2016).

Overall, the study introspected the major reasons for the formulation of urban voids and the challenges raised by it, however, most of the issues were related to poor decision-making and the severity of issues posed by the voids. Hence, the review infers the need for a methodical framework to analyze the issues of these void lands in depth. This raised the first research concern.

The urban voids are the unutilized, underutilized, vacant, defunct, undeveloped, unstructured, abandoned lands having obsolete infrastructure which pose tangible or intangible threats to the urban fabric (Aruninta, 2005; Pagano and Bowman, 2000; Raisoni, H, Singh et al., 2019; Trancik, 1991). However, these often disregarded or marginalized public spaces can act as catalytic, opportunistic, and resourceful spaces which have tremendous potential for the micro-level revival of the urban realm (Omar and Saeed, 2019; Trancik, 1991).

They can offer important but unrealized resources for sustainable urban development (Rahmann, 2011). Further, with their optimal utilization and monetization, the void plots can act as a catalyst for the local economy (Peterson, 2009; Raisoni, Harshad, Verma et al., 2023).

However, only cosmetic treatment would barely be helpful for the holistic improvement of such public lands and it requires holistic efforts for their revival (Accordino and Johnson, 2000; Boonstra and Boelens, 2011; De Silva, 1998). Several researchers globally have attempted different prominent approaches for the public space revival, hence, it was pertinent to examine these frameworks and their challenges by observing the lacunae within them.

The ‘Urban Void Revitalization Framework’ mentioned by the scholars (Hashem, Wahba et al., 2022) presented a three-pillar multi-criteria approach for urban void revitalization namely Void Design Components, User needs and activities and Technological Installations. Whereas, the logical and scientific method for the evaluation of urban public spaces was proposed by urban designer Vikas Mehta (Mehta, 2014). He assessed them based on four significant criteria of inclusiveness, meaningfulness, safety, comfort and pleasurability in an empirical way using the ‘Good Public Space Index (GPSI)’ meant to measure the quality of public spaces. In addition, Palicki (2016) proposed a framework to analyze Public Spaces with a Social perspective by a multi-criteria analysis framework with factors influencing the performance of public spaces such as Social, Economic, Cultural and Urban factors with 22 prominent sub-criteria. The Scholar (Mandeli, 2019) proposed a conceptual framework for the ‘Appropriateness of Public Open Spaces’. According to this framework, the Environmental Factors, Human Nature and Social Milieu are the three unique constructs which are part of the ‘Man-Environmental Relationship’ which can result in a better outcome, especially in residential neighbourhoods.

Additionally, the participatory citizen engagement for urban planning initiatives like Tactical Urbanism (Sasser, 2017), Do It Yourself approaches (Dittmar, 2020), Guerrilla urbanism (Hou, 2010) etc. are also attempted to have a bottom-up approach to urban planning (Raisoni, Harshad and Rajaullah, 2018; Sandin, 2020; Sharma, B., Sharma et al., 2022).

However, the revival frameworks studied were designed either for specific functions or contexts with more focus on qualitative parameters. The empirical approaches were less attempted. Further, very few frameworks were comprehensive for analyzing the potential and revival of void public spaces in a scientific and non-subjective way. Overall, the study introspected the need to tap the hidden potentials of multifaceted urban voids in a methodical approach. This raised the second research concern.

Formulation of Research Objectives

The review of the literature raised two important concerns or gaps in the domain of urban void revitalization. The first gap thrusts on the need for a ‘Methodical Framework’ for multi-criteria assessment of urban voids to know their development potential. The second gap propelled the deficiency of rational and methodical decision-making process for the ‘Prioritization’ of urban voids using the development potential scores. Considering these two significant gaps, the following two research objectives were formulated.

a) To assess the development potential of urban voids using the Inventory.

b) To formulate a robust methodical prioritization model based on the potentials of urban voids.

Ultimately, the research addresses the gaps in the domain of urban void revitalization by providing an inventory-based prioritization model for selecting urban voids based on their potential.

Identification of Parameters for Inventory

To fulfil the above research objective of assessing the development potential of urban voids at the city-section level, the review informed the need for an Inventory. Accordingly, various criteria under prominent parameters were extracted from the detailed literature review and were further categorized into eight fundamental parameters as mentioned in Table 1 and Table 3.

Table 1. List of Parameters with References

Sr. No.	Parameters	References
1	Physical Parameters	Laskari (2014); Zakariya, Harun et al. (2014); Abdul Rahman, Ghani et al. (2020); Carmona (2021); Garau (2015); Hashem, Wahba et al. (2022); Tehlova, Veillard et al. (2019).
2	Spatial Parameters
3	Social Parameters	Ballaney, Bertaud et al. (2013); Eriawan and Setiawati (2017); Kozlova and Kozlov (2017); Palicki (2016)
4	Economical Parameters
5	Urban Parameters	Abdul Rahman, Ghani et al. (2020); Garau (2015); Hashem, Wahba et al. (2022); Lynch (1960); Palicki (2016); Tehlova, Veillard et al. (2019); Trancik (1991)
6	Environmental Parameters
7	Cultural Parameters	Bentley, McGlynn et al. (2013); Eriawan and Setiawati (2017); Kozlova and Kozlov (2017); Lynch (1960); Sanei, Khodadad et al. (2017); Trancik (1991)
8	Behavioural Parameters

Different researchers across the world exercised many of these parameters and criteria to analyse the urban voids depending upon their physical context, the condition of voids, and their impact on the urban realm. The list was enhanced after a pilot study was conducted within the delineated study area to get more pragmatic insights and comprehensiveness. The inventory was finalized through the Expert Opinion Survey described in the Research Methodology section.

Research Methodology

Considering the research concerns and objectives, the diagrammatic methodology given in Figure 2 was formulated to prepare the inventory-based prioritization model.

Figure 2. Research Methodology for prioritization using Urban Void Analysis Index (UVAI)

Using the above methodology, an eight-step approach was articulated to achieve the desired outcomes given as follows.

Step 1. Assessment of criteria and parameters with an Expert Opinion Survey.

Step 2. Finalization of criteria and parameters

Step 3. Internal Consistency and Reliability Test

Step 4. Finalization of the Inventory

Step 5. Primary Surveys of Urban Voids within the selected study area

Step 6. Identifying weights through AHP Surveys.

Step 7. Prioritization through TOPSIS, VIKOR and MOORA.

Step 8. Establishing the correlation between results.

Additionally, this section provides the details of the Expert Opinion Surveys, Finalization of criteria and parameters, Internal Consistency and Reliability, Final Inventory Framework and Hybrid Multi-Criteria Decision Making (MCDM) mathematical model as follows.

Expert Opinion Survey

An Expert Opinion Survey was conducted in two stages, firstly to refine and finalize the parameters and criteria for the ‘Inventory Framework’ (Table 2). It ensured the relevance and credibility of the identified parameters and criteria aimed for the assessment of Urban Voids. Secondly, to finalize the weightages of these parameters and criteria by employing the ‘Analytical Hierarchy Process’ (AHP) which uses a pairwise comparative approach.

To get comprehensive insights about both these stages, they were exposed to the inspection of 18 experts (18 out of 25 responded) who were carefully selected from the domain related to urban planning Consultancy (33%), Government (39%) and Academia (28%) from different states having a minimum experience of 10 years. About 72% of experts were qualified professionals in Urban Planning, while 28% represented other professions.

As both these stages were part of a common interlinked agenda of Urban Void Assessment and derivation of Weightages, the same experts were continued for both stages. It was more advantageous to continue them as they were aware of the intention of the research as well as they represented a cross-section of the domain of urban planning with a sound experience.

Finalization of Criteria and Parameters

To finalize criteria and parameters, Expert Opinion Surveys were performed with a five-point Likert scale as comparatively it was simple, timesaving, considered neutral, possessed clear distinction with lower margin of errors, had a relative base, ideal for larger questionnaires, and aligned with many scientific research and benchmarks (Bouranta, Chitiris et al., 2009; Buttle, 1996; Malhotra and Peterson, 2006). The results of the survey were analysed using the interpretations given in Table 2.

Table 2. Likert Scale Interpretation

Likert Scale Value	Description	Mean Range	Interpretation for Research
1	Not Important at all	1.00-1.80	Criteria Not Selected
2	Slightly Important	1.81-2.60	Criteria Not Selected
3	Moderately Important	2.61-3.40	Criteria Selected
4	Very Important	3.41-4.20	Criteria Selected
5	Extremely Important	4.21-5.00	Criteria Selected

The criteria having a score less than 2.60 and high standard deviation were not selected for the research purpose. The results are shown in Table 3 wherein ‘S’ indicates the criteria ‘Selected’ and ‘NS’ indicates ‘Not Selected’.

Table 3. Selection of criteria through Expert Opinion Survey

Para-meters	Code	Criteria	Mean	SD	Variance	Importance	Status
Physical	1	Size, Shape, and Slope of the Plot	4.22	0.71	0.5062	Extr. Imp	S
	2	Physical & Social Infra availability on Plot	4.17	0.83	0.6944	Very Imp	S
	3	Accessibility to the Plot	3.72	0.87	0.7562	Very Imp	S
	4	Type of built-up on Plot	1.78	0.79	0.6173	Not Imp	NS
	5	Width of Approach Road	3.78	0.71	0.5062	Very Imp	S
Spatial	6	Current Use of the Plot	3.17	0.69	0.4722	Mod. Imp	S
	7	Reason for the creation of Urban Voids	2.39	0.83	0.6821	Slightly Imp	NS
	8	FSI Utilization in the Plot	3.78	0.71	0.5062	Very Imp	S
	9	Legal Status of the Plot	4.22	0.63	0.3951	Extr. Imp	S
Urban	10	Legibility of Site from Approach Road	3.39	0.89	0.7932	Very Imp	S
	11	Imageability of Site	3.44	0.83	0.6914	Very Imp	S
	12	Level & Type of Enclosure	4.06	0.70	0.4969	Very Imp	S
	13	Sense of belongingness of Site	2.94	0.78	0.6080	Mod. Imp	S
	14	Attractiveness of the place	2.00	0.82	0.6667	Slightly Imp	NS
Environmental	15	Level of Environmental sensitivity of the site	2.94	0.62	0.3858	Mod. Imp	S
	16	Availability of Green cover	3.67	0.82	0.6667	Very Imp	S
	17	Pollution Level	4.06	0.85	0.7191	Very Imp	S
	18	Level of hazardousness for health by the site	1.78	0.79	0.6173	Not Imp	NS
Cultural	19	Diversity of Users	1.94	0.78	0.6080	Slightly Imp	NS
	20	Level of Permanence of Space	3.06	0.70	0.4969	Mod. Imp	S
	21	Level of Vulnerability on/by Site	1.83	0.76	0.5833	Slightly Imp	NS
	22	Usage of the Plot by People	3.61	0.89	0.7932	Very Imp	S
	23	Level of multi-usage of Plot	1.89	0.74	0.5432	Slightly Imp	NS
Behavioural	24	Level of Community Interaction with Site	3.39	0.89	0.7932	Mod. Imp	S
	25	Level of Memory associated with the site	1.94	0.70	0.4969	Slightly Imp	NS
	26	Effect of Community on Plot	3.78	0.85	0.7284	Very Imp	S
	27	Intensity of use of land	2.33	1.29	1.6667	Slightly Imp	NS
Social	28	Safety Majors	4.00	0.75	0.5556	Very Imp	S
	29	Intensity of Social use	2.28	0.80	0.6451	Slightly Imp	NS
	30	Crime Rates	3.83	0.83	0.6944	Very Imp	S
	31	Social Accessibility to All Groups	3.44	0.76	0.5802	Very Imp	S
	32	Level of restrictions on site	2.22	0.85	0.7284	Slightly Imp	NS
Economic	33	Economy Generation on/by the Plot	3.28	0.65	0.4228	Mod. Imp	S
	34	Possibility of Economic Development	3.50	0.96	0.9167	Very Imp	S
	35	Market Value of the Plot	4.00	0.75	0.5556	Very Imp	S
	36	Offer of the space	2.39	0.83	0.6821	Slightly Imp	NS

Internal Consistency and Reliability Test

The internal consistency and reliability of the set of criteria were analyzed using Cronbach’s Alpha (α) with the formula given below, where α is coefficient alpha, k is the total number of criteria, Vc is the variance of individual criteria, and Vt is the variance of a total of all criteria on the list.

$𝛂=\left[\frac{k}{k-1}\right][1-\frac{{\sum }_{c=1}^k{V}_c}{V_t}]$

It assesses the closeness of criteria as a group and checks whether they all measure the same underlying construct or not as well as reliably measures the intended research purpose. The α score ranges between 0 to 1 where, the Internal Consistency α ≥ 0.90 is considered as ‘Excellent’, 0.90 > α ≥ 0.80 is ‘Good’, 0.80 > α ≥ 0.70 is ‘Acceptable’, 0.70 > α ≥ 0.60 is ‘Questionable’, 0.60 > α ≥ 0.50 is ‘Poor’ and 0.50 > α is ‘Unacceptable’.

Table 4. Calculation of Reliability and Internal Consistency

Variables	Description	Values	Results
k	Number of Criteria	36	Internal Consistency was found to be good.
$${𝐕}_{𝐜}$$	Individual Criteria Variance	23.27
$${𝐕}_{𝐭}$$	The variance of the total of all Criteria	127.28
α	Cronbach's Alpha	0.8405

It assesses the closeness of criteria as a group and checks whether they all measure the same underlying construct or not as well as reliably measure the intended research purpose. The α score ranges between 0 and 1 where, the Internal Consistency α ≥ 0.90 is considered as ‘Excellent’, 0.90 > α ≥ 0.80 is ‘Good’, 0.80 > α ≥ 0.70 is ‘Acceptable’, 0.70 > α ≥ 0.60 is ‘Questionable’, 0.60 > α ≥ 0.50 is ‘Poor’ and 0.50 > α is ‘Unacceptable’.

Table 4 mentions the final α score as 0.8405 which falls between 0.90 > α ≥ 0.80 range indicating a ‘Good’ amount of internal consistency amongst the Expert Opinions, making the overall analysis more reliable.

Finalization of the Inventory

The experts suggested the additional six criteria as important criteria as they could add positive value to the research. They are Mode to reach the Plot, Locational Advantage, Age of the Void Plot, Maintenance Status of the Plot, Prone to Natural Calamities, and Area Statement Rates of the Plot (ASR). These newly suggested criteria were carefully scrutinized and accommodated within the selected criteria considering their potential and relevance for the study.

Further, the eight parameters (Table 3) were consolidated into four central parameters arranged logically with their associated criteria and sub-criteria. They were modulated and organized hierarchically ultimately giving a ‘Final Inventory Framework’ (Table 5). This was pivotal in understanding the interrelationships between the parameters, criteria, and sub-criteria which was further utilized for the Analytical Hierarchy Process (AHP) analysis.

Table 5. Final Inventory for Urban Void Analysis with Hierarchical Framework

Goal	Parameters	Criteria	Sub-Criteria
	Level 1	Level 2	Level 3
UVAI (Urban Void Analysis Index)	A. PSP (Physical & Spatial Parameter)	PS 1. Spatial Aspects of the Plot	PS 1.1 Topography of the Plot
			PS 1.2 Geometry of the Plot
			PS 1.3 Size of the Plot
			PS 1.4 FSI Utilization in the Plot
		PS 2. Infrastructural Aspects	PS 2.1 Physical Infrastructure Availability on the Plot
			PS 2.2 Social Infrastructure Availability on/near the Plot
		PS 3. Physical Accessibility	PS 3.1 Type of Approach Road to the Plot
			PS 3.2 Width of Approach Road
			PS 3.3 Mode to reach the Plot
		PS 4. Land Status	PS 4.1 Legal Status of the Plot
			PS 4.2 Current Use of the Plot
			PS 4.3 Locational Advantage
			PS 4.4 Age of the Void Plot
			PS 4.5 Maintenance Status of the Plot
	B. SEP (Social & Economical Parameters)	SE 1. Social Accessibility	SE 1.1 Social Accessibility to All Groups
			SE 1.2 Safety
			SE 1.3 Crime Rate on Plot
		SE 2. Land Value	SE 2.1 Market Value of the Plot
			SE 2.2 Ready Reckoner Value of the Plot (ASR)
		SE 3. Economy Generation	SE 3.1 Economy Generation on/by the Plot
			SE 3.2 Possibility of Economic Development
	C. UEP (Urban & Environmental Parameters)	UE 1. Environmental Aspects	UE 1.1 Pollution Level
			UE 1.2 Availability of Green Cover
			UE 1.3 Impact of Nearby Environmental Sensitive Areas on the Plot
			UE 1.4 Prone to Natural Calamities
		UE 2. Level of Enclosure	UE 2.1 Site Boundaries
			UE 2.2 Sense of Belongingness of the Plot
		UE 3. Visual Accessibility	UE 3.1 Imageability of Plot
			UE 3.2 Legibility of Site from Approach Road
	D. CBP (Cultural & Behavioural Parameters)	CB 1. Usage of the Plot	CB 1.1 Level of Permanence of the Space
			CB 1.2 Level of Usage of the Plot by the People
		CB 2. Community Behaviour	CB 2.1 Impact of Nearby Community on the Plot
			CB 2.2 Level of Community Interaction with Plot

Development of a Hybrid MCDM Approach

The Inventory comprised multiple parameters, criteria, and sub-criteria of different genres which informed the necessity of a hybrid MCDM approach (Rane, Achari et al., 2023). The Inventory and research objectives emphasized a combination of methods for calculating the potential weights and the ranking of urban voids to get the most efficient solution.

Eventually, the Analytical Hierarchy Process (AHP) (Saaty, Thomas L., 2001; Saaty, Thomas L, 2008) was selected for the weight calculations due to its pairwise and structured comparative approach encompassing multiple criteria. AHP offers several advantages over Fuzzy AHP (FAHP) including simplicity, time efficiency, and consistency in covering broad objectives of the study. Unlike FAHP, which tends to be more complex and time-consuming, AHP's use of a 9-point scale inherently accommodates a sufficient level of fuzziness (Chan, Sun et al., 2019). Thus, AHP was deemed the preferred method over FAHP.

Further, TOPSIS, VIKOR, and MOORA were advantageous for their ability to handle multi-criteria decision-making in prioritizing urban voids effectively. They accommodate both positive and negative criteria with different decision-maker preferences and offer comprehensive rankings. SAW and WP methods are less suitable due to their inability to address trade-offs between criteria and handle conflicting objectives in complex decision-making like void prioritization (Kraujalienė, 2019; Zakariya, Harun et al., 2014).

The combination of AHP with three highly recognized methods of AHP-TOPSIS (Technique for Order of Preferences by Similarity to Ideal Solution) (Aliani, Ghanbari Motlagh et al., 2021; İç, 2012; Raju, Murali et al., 2020; Ramya and Devadas, 2019), AHP-VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) (Ramavandi, Darabi et al., 2021; Shokri, Ashjari et al., 2013; Wibawa, Fauzi et al., 2019), and AHP-MOORA (Multi-Objective Optimization based on Ratio Analysis) (Madic, Radovanovic et al., 2015; Moslem and Çelikbilek, 2020; Raju, Murali et al., 2020; Satoglu and Türkekul, 2021) methods were finalized. This facilitated the provision of intended UVAI with a higher correlation in ranking the voids using Spearman’s correlational analysis.

This amalgamation allowed the holistic and rigorous evaluation of voids considering the ‘ideal solutions’ ‘compromised solutions’ and ‘optimized solutions’ respectively, thus providing multiple choices for the decision makers as required. It ensured that the chosen combination was not only robust but also yielded consistent results according to the inherent characteristics of voids under introspection.

Hybrid Mathematical Model

To get the intended outputs, the finalized hybrid MCDM approach with a combination of AHP with TOPSIS, VIKOR, and MOORA is detailed as follows.

AHP Method for the calculation of weights

(E1) The inventory criteria were assessed using Saaty’s Scale (Saaty, Thomas L., 2001) with the Expert Opinion Surveys designed with a hierarchical structure. The scale provided the intensity of the relative importance of several criteria

(E2) Pairwise comparison was performed to get maximum eigenvalue.

(E3) While calculating the weights, the consistency ratio was kept between 0 and 1 (0 < CR < 0.1) ultimately providing the aggregate parameter, criteria, and sub-criteria weights to be used for prioritization in further steps.

TOPSIS Method for prioritization with an Ideal solution

(E4) The TOPSIS method (Hwang, C.-L. and Lin, 2012) involved the construction of standardized decision matrix A for the comprehensive assessment questions with n evaluation units and m evaluation indexes, its decision matrix (A), and the construction of a Normalized matrix (R).

Rij = xij / (Σx2ij), for i = 1, …, m; j = 1, …n.

(E5) Constructed weighted normalized decision matrix (V) using weights derived from AHP using

Vij=wj * Rij, i= 1, 2,...,n, j= 1,2,...m.

(E6) Determined the Positive ideal solution (A+) and Negative ideal solution (A-) using the following formula.

(E7 and E8) Calculated the Separation Measures (Si+) and (Si-) from the Positive and Negative ideal solution respectively for each alternative.

(E9) Calculated the Relative Closeness Coefficient (Ci) of each alternative to the ideal solution.

(E10) Applied the parameter weight and prioritized the Alternatives in the descending order of Ci giving an Ideal Solution.

VIKOR Method for prioritization with a compromised solution

The VIKOR method has the following compromised ranking algorithm given by Opricovic and Tzeng (2004).

(E11) Calculated the best ${f^{*}}_i$ and the worst ${f^{-}}_i$ values of all criteria and sub-criteria for all the selected urban voids. For all beneficial criteria, maximum value of ${f^{*}}_i$ and minimum value of ${f^{-}}_i$ was considered and vice-versa was considered for all non-beneficial criteria.

(E12 and E13) Then the value of $S_j$ and $R_j$ was computed using the following formula where j = 1,2,3…j.

  

$${𝐒}_{𝐣}=\sum _{i=1}^n{w}_i\frac{({f^{*}}_{\mathrm{\text{i}}}-f_{\mathrm{\text{ij}}})}{({f^{*}}_{\mathrm{\text{i}}}-{f^{-}}_i)}{\mathrm{\text{R}}}_{𝐣}=\underset{i}{max}[\frac{w_i({f^{*}}_{\mathrm{\text{i}}}-f_{\mathrm{\text{ij}}})}{({f^{*}}_{\mathrm{\text{i}}}-f_{\mathrm{\text{ij}}})}]$$

where $f_{\mathrm{\text{ij}}}$ is the value of j alternatives for i criteria, $S_j$ represents concordance (Utility measure) and $R_j$ represents discordance (regret measure) of j alternatives of voids; $w_i$ is the weight of each criterion calculated using AHP.

(E14) Calculated the values for $S^{*}$ , $R^{*}$ , $S^{-}$ and $R^{-}$

$S^{*}=min\left(S_j\right),$ $S^{-}=max$ ( $S_j)$ , $R^{*}=min\left(R_j\right),$ $R^{-}=max$ ( $R_j)$

(E15) The voids were $$ ranked by the value of $Q_j$ derived as follows.

  

$${𝐐}_{𝐣}=v(S_j-S^{*})/(S^{-}-S^{*})+(1-v)(R_j-R^{*})/(R^{-}-R^{*})$$

where $Q_j$ represents the solution of alternatives, $v$ is the weight for the strategy of maximum group utility and $(1-v)$ is the weight of individual regret. Usually, $v$ is taken as 0.5, however, it may have a value between 0 and 1.

(E16) The final obtained solution provides a maximum utility of the majority ${(S}_j)$ and a minimum individual regret of the opponent ${(R}_j)$ . These results are integrated into $Q_j$ for a compromised solution and ranked from the lowest to the highest scores in an ascending manner.

MOORA Method for optimized solution calculation and ranking the urban voids

(E17) MOORA method (Alinezhad and Khalili, 2019) commences with formulating the aim and identifying the relevant criteria for evaluating the available urban voids and the decision matrix ‘X’ is prepared.

$x_{\mathrm{\text{ij}}}$ is the potential measure of i-th alternative voids with respect to the j-th criterion, m is the number of alternatives and n is the number of criteria. MOORA method considers both the beneficial and non-beneficial criteria to rank the alternatives using the weights derived from AHP.

(E18) $x_{\mathrm{\text{ij}}}$ is normalized index range between 0 and 1 to derive the ratio system.

  

$${{x^{*}}_{\mathrm{\text{ij}}}=x}_{\mathrm{\text{ij}}}/\sqrt{\sum _{i=1}^m{x}_{\mathrm{\text{ij}}}},(j=1,2,\dots .n)$$

(E19) The value of $x_{\mathrm{\text{ij}}}$ is added in the case of beneficial criteria (B) and subtracted in the case of non-beneficial criteria (NB). The optimization formula is given below where g is the number of criteria to be maximized (beneficial criteria) and n is the number of criteria to be minimized (non-beneficial criteria). $y_{\mathrm{\text{i}}}$ is the composite score of voids derived considering all inventory criteria.

  

$$y_{\mathrm{\text{i}}}=\sum _{j=1}^g{w}_{\mathrm{\text{j}}}\mathrm{\text{.}}{x^{*}}_{\mathrm{\text{ij}}}-\sum _{j=g+1}^n{w}_{\mathrm{\text{j}}}\mathrm{\text{.}}{x^{*}}_{\mathrm{\text{ij}}}$$

(E20) The value of $y_{\mathrm{\text{i}}}$ may be positive or negative depending on the total number of beneficial and non-beneficial criteria in the decision matrix. The ranking of these values is decided in a descending order where the best alternative has the highest $y_{\mathrm{\text{i}}}$ value and the worst alternative has the lowest $y_{\mathrm{\text{i}}}$ value.

Correlation among the ranking results of TOPSIS, VIKOR and MOORA values

(E21) The Spearman’s Correlation Coefficient ( $\rho )$ is calculated among the ranks derived from TOPSIS, VIKOR and MOORA methods. It evaluates the monotonic data derived from the rank order data where +1 indicates perfect positive correlation, -1 indicates perfect negative correlation, and 0 indicates non-monotonic relationship. It is calculated with the following formula.

  

$$𝛒=1-\frac{6\Sigma d^2}{n(n^2-1)}$$

Where, ρ is Spearman’s Rank Correlation Coefficient, d is the difference between the ranking of each method, n is the number of paired observations.

Details of the Study Area

To achieve the desired objectives, the Indian city of Pimpri-Chinchwad Municipal Corporation (PCMC) located on the Deccan Plateau of the Western Maharashtra State was identified as an appropriate case. It is famous for its rich cultural heritage, automobile and manufacturing industries and IT parks spread over 181 sq. km. of an area with a population of 17,27,692 as per the 2011 census and expected to grow to around 19,20,330 by the year 2023. Being part of the urban agglomeration of the Pune Metropolitan Area, the city acts as a twin city and a prominent growth engine with major growth centres namely Akurdi, Bhosari, Chinchwad, Nigadi, Sangavi and Pimpri along with new magnets of Moshi, Pimpale-Saudagar, Pimpale-Nilakh, Ravet, Wakad etc. adding more migrants to it. Its transition with the sequential overlays of development from a refugee town to a satellite town to an independent town in about six decades generated the morphology of the city. Hence, multiple authorities have been assigned different roles to develop one of the fastest-growing cities.

Details of the selected zone within the Study Area

To study the void scenario, a pilot case of the Akurdi Zone was chosen having a population of 1,07,440 people as per the 2011 census spread over 8.7 sq. km. area. It represented the characteristic section of the city including the core area, urban area and sub-urban area. Also, it had a combination of planned and unplanned areas found to have 29 vacant and underutilized void lands (Figure 1). The introspection ascertained that these voids were primarily the result of the decadal shift of development focus and resources, morphological overlays, conflict in multi-authoritative decision-making, planning issues, encroachments, inequitable distribution of public reservation lands etc.

Out of all 29 plots, only five representative samples were chosen for the present research considering the Age of the void, Plot Area, Accessibility, Availability of Physical Infrastructure, Landuse and Imageability as the major selection criteria. The voids were marked as per the records available with ULB, accordingly, the primary and secondary data was collected as per the need of the Inventory Framework.

Figure 1. Akurdi Zone Map showing identified and shortlisted urban void plots

Details of Primary Surveys conducted within the selected zone

To get a nuanced understanding of the selected five sample urban voids, about 50 Public Opinion surveys were conducted employing a meticulous questionnaire based on the Final Inventory along with Reconnaissance surveys. Approximately, 8-12 samples per void were surveyed through the systematic sampling method in the Akurdi zone of PCMC.

About 22.3% of respondents were Postgraduates and above, 67.5% were graduates and 10.2% were educated up to the 10th class and belonged to low, medium and higher-income groups. The age of the respondents was 15-25 years (31.8%), 25-60 years (41.7%) and 60 years above (26.5%). About 71.3% of the respondents owned their houses and the remaining 28.7% stayed in rented houses. The questionnaire incorporated questions related to the type of usage, safety and crime issues, environmental issues, physical features, imageability and behavioural issues to collect precise and intended data about urban voids.

Results of tHE Evaluation of Urban Voids Using Hybrid MCDM Methods

The selected five urban voids were analyzed in depth employing the hybrid MCDM methods as discussed in the Methodology Section. The weightages were derived using AHP and prioritization using TOPSIS, VIKOR and MOORA methods detailed as follows.

Identifying Weightages through AHP Surveys

The task of the derivation of weights for Parameter (Pw), Criteria (Cw), and Sub-criteria (SCw) was accomplished by employing the AHP-based Expert surveys. It provided a robust foundation which ultimately derived meaningful and contextually relevant Final weights (Fw) as shown in Table 7. The versatility of AHP coupled with experts, ensured a holistic and informed determination of weights used for the subsequent analyses in the study.

Table 6. Parameter weights (Pw) derived through AHP

Matrix	PSP	SEP	UEP	CBP	Eigenvector Normalized Value (W)
PSP	1	3	1 2/3	3 2/3	0.444
SEP	1/3	1	2/3	2 1/7	0.178
UEP	3/5	1 1/2	1	4	0.287
CBP	1/4	1/2	1/4	1	0.091
				CR	0.017

Based on the Expert Surveys, Table 6 provide one set of calculations for AHP weightages for all parameters using (E1), (E2), and (E3) equations as given in the mathematical model. Out of all four key parameters, the highest weightage of 0.444 of the Physical and Spatial Parameter (PSP) indicated that it had a significant influence on the decision-making process. The Urban and Environmental Parameter (UEP) and Socio-Economic Parameter (SEP) received 0.287 and 0.178 weightages respectively becoming the prominent factors in decision-making. The Cultural and Behavioural Parameter (CBP) had the lowest weightage of 0.091 indicating comparatively less importance.

Similarly, the criteria and sub-criteria weights were also calculated and final weights were derived (Table 7). Each void plot was surveyed and tallied with the final weights in further steps yielding prioritization of voids.

Table 7. Final AHP weights for Parameters, Criteria and Sub-Criteria

Parameters	Pw	Criteria	Cw	Sub-Criteria	SCw	Fw
A. PSP (Physical & Spatial Parameters)	0.444	PS1	0.314	PS 1.1	0.172	0.054
				PS 1.2	0.313	0.098
				PS 1.3	0.248	0.078
				PS 1.4	0.267	0.084
		PS2	0.202	PS 2.1	0.781	0.158
				PS 2.2	0.219	0.044
		PS3	0.205	PS 3.1	0.378	0.077
				PS 3.2	0.358	0.073
				PS 3.3	0.264	0.054
		PS4	0.279	PS 4.1	0.354	0.099
				PS 4.2	0.192	0.054
				PS 4.3	0.149	0.042
				PS 4.4	0.178	0.050
				PS 4.5	0.127	0.035
B. SEP (Social & Economical Parameters)	0.178	SE1	0.375	SE 1.1	0.339	0.127
				SE 1.2	0.380	0.143
				SE 1.3	0.281	0.105
		SE2	0.319	SE 2.1	0.585	0.187
				SE 2.2	0.415	0.132
		SE3	0.306	SE 3.1	0.516	0.158
				SE 3.2	0.484	0.148
C. UEP (Urban & Environmental Parameters)	0.287	UE1	0.425	UE 1.1	0.363	0.154
				UE 1.2	0.191	0.081
				UE 1.3	0.261	0.111
				UE 1.4	0.185	0.079
		UE2	0.281	UE 2.1	0.634	0.178
				UE 2.2	0.366	0.103
		UE3	0.294	UE 3.1	0.487	0.143
				UE 3.2	0.513	0.151
D. CBP (Cultural & Behavioural Parameters)	0.091	CB1	0.581	CB 1.1	0.533	0.310
				CB 1.2	0.467	0.271
		CB2	0.419	CB 2.1	0.455	0.191
				CB 2.2	0.545	0.228

Prioritization through TOPSIS, VIKOR and MOORA

Each selected void plots namely UV1, UV2, UV3, UV4 and UV5 were physically surveyed giving individual AHP weights. Table 8 provides the Normalized Weightage Matrix (NDM) and Final weights (Fw) for each urban void. NDM was calculated by Equation (E4) and used commonly for all three ranking methods. This processed data was further used for TOPSIS, VIKOR and MOORA methods to derive the appropriate ranking.

Table 8. Normalized Weightage Matrix along with Sub-criteria wise AHP weights

Criteria	Sub-Criteria	UV1	UV2	UV3	UV4	UV5	AHP Weight
PS1	PS 1.1	0.0704	0.0704	0.0391	0.0704	0.0704	0.0540
	PS 1.2	0.0747	0.0747	0.0415	0.0747	0.0747	0.0980
	PS 1.3	0.1245	0.1245	0.0138	0.0692	0.0692	0.0780
	PS 1.4	0.0830	0.0830	0.0830	0.0830	0.0830	0.0840
PS2	PS 2.1	0.3330	0.4022	0.4022	0.3330	0.3330	0.1580
	PS 2.2	0.0906	0.0906	0.0503	0.0503	0.0705	0.0440
PS3	PS 3.1	0.0731	0.0731	0.0731	0.0731	0.0731	0.0770
	PS 3.2	0.1430	0.0795	0.0795	0.0477	0.0477	0.0730
	PS 3.3	0.0632	0.0885	0.0885	0.0632	0.0632	0.0540
PS4	PS 4.1	0.0731	0.0731	0.0731	0.0244	0.0244	0.0990
	PS 4.2	0.0732	0.0732	0.0732	0.0244	0.0244	0.0540
	PS 4.3	0.0703	0.0703	0.0703	0.0703	0.0703	0.0420
	PS 4.4	0.0662	0.0662	0.0662	0.0662	0.0662	0.0500
	PS 4.5	0.0797	0.0159	0.0159	0.0159	0.0159	0.0350
SE1	SE 1.1	0.0434	0.0782	0.0782	0.0782	0.0782	0.1270
	SE 1.2	0.0108	0.0542	0.0542	0.0542	0.0542	0.1430
	SE 1.3	0.0458	0.0458	0.0458	0.0458	0.0458	0.1050
SE2	SE 2.1	0.0911	0.0716	0.0481	0.0699	0.0481	0.1320
	SE 2.2	0.0997	0.0784	0.0527	0.0861	0.0527	0.1870
SE3	SE 3.1	0.0141	0.0141	0.0141	0.0704	0.0704	0.1580
	SE 3.2	0.0448	0.0806	0.0448	0.0448	0.0806	0.1480
UE1	UE 1.1	0.0377	0.0628	0.1005	0.0377	0.0377	0.1540
	UE 1.2	0.0579	0.0413	0.0744	0.0744	0.0744	0.0810
	UE 1.3	0.0562	0.0562	0.0562	0.0562	0.0562	0.1110
	UE 1.4	0.0457	0.0457	0.0823	0.0823	0.0823	0.0790
UE2	UE 2.1	0.0810	0.0810	0.0810	0.0810	0.0090	0.1780
	UE 2.2	0.0590	0.0590	0.0590	0.0590	0.0590	0.1030
UE3	UE 3.1	0.0144	0.1296	0.0720	0.0144	0.0144	0.1430
	UE 3.2	0.0731	0.0731	0.0731	0.0731	0.0731	0.1510
CB1	CB 1.1	0.0558	0.0558	0.0558	0.0558	0.0558	0.3100
	CB 1.2	0.0514	0.0514	0.0514	0.0514	0.0514	0.2710
CB2	CB 2.1	0.0684	0.0684	0.0684	0.0137	0.0684	0.1910
	CB 2.2	0.0103	0.0517	0.0517	0.0517	0.0517	0.2280

Ranking using TOPSIS Method for Ideal Solution

Table 9 provides the Weighted Normalized Decision Matrix (WNDM) calculated using Equation (E5) and values from Table 8. It also provides the Positive ideal solution (A+) and Negative ideal solution (A-) derived from Equation (E6) considering the beneficial (B) and non-beneficial (NB) aspects.

Table 9. TOPSIS: WNDM and positive (A+) and negative (A-) ideal solutions

Criteria	Sub-Criteria	UV1	UV2	UV3	UV4	UV5	B/ NB	A+ (Max)	A- (Min)
PS1	PS 1.1	0.0038	0.0038	0.0021	0.0038	0.0038	B	0.0038	0.0021
	PS 1.2	0.0073	0.0073	0.0041	0.0073	0.0073	B	0.0073	0.0041
	PS 1.3	0.0097	0.0097	0.0011	0.0054	0.0054	B	0.0097	0.0011
	PS 1.4	0.0070	0.0070	0.0070	0.0070	0.0070	B	0.0070	0.0070
PS2	PS 2.1	0.0526	0.0636	0.0636	0.0526	0.0526	B	0.0636	0.0526
	PS 2.2	0.0040	0.0040	0.0022	0.0022	0.0031	B	0.0040	0.0022
PS3	PS 3.1	0.0056	0.0056	0.0056	0.0056	0.0056	B	0.0056	0.0056
	PS 3.2	0.0104	0.0058	0.0058	0.0035	0.0035	B	0.0104	0.0035
	PS 3.3	0.0034	0.0048	0.0048	0.0034	0.0034	B	0.0048	0.0034
PS4	PS 4.1	0.0072	0.0072	0.0072	0.0024	0.0024	B	0.0072	0.0024
	PS 4.2	0.0040	0.0040	0.0040	0.0013	0.0013	B	0.0040	0.0013
	PS 4.3	0.0030	0.0030	0.0030	0.0030	0.0030	B	0.0030	0.0030
	PS 4.4	0.0033	0.0033	0.0033	0.0033	0.0033	B	0.0033	0.0033
	PS 4.5	0.0028	0.0006	0.0006	0.0006	0.0006	B	0.0028	0.0006
SE1	SE 1.1	0.0055	0.0099	0.0099	0.0099	0.0099	B	0.0099	0.0055
	SE 1.2	0.0016	0.0078	0.0078	0.0078	0.0078	B	0.0078	0.0016
	SE 1.3	0.0048	0.0048	0.0048	0.0048	0.0048	B	0.0048	0.0048
SE2	SE 2.1	0.0120	0.0094	0.0063	0.0092	0.0063	B	0.0120	0.0063
	SE 2.2	0.0186	0.0147	0.0098	0.0161	0.0098	B	0.0186	0.0098
SE3	SE 3.1	0.0022	0.0022	0.0022	0.0111	0.0111	B	0.0111	0.0022
	SE 3.2	0.0066	0.0119	0.0066	0.0066	0.0119	B	0.0119	0.0066
UE1	UE 1.1	0.0058	0.0097	0.0155	0.0058	0.0058	B	0.0155	0.0058
	UE 1.2	0.0047	0.0033	0.0060	0.0060	0.0060	NB	0.0033	0.0060
	UE 1.3	0.0062	0.0062	0.0062	0.0062	0.0062	NB	0.0062	0.0062
	UE 1.4	0.0036	0.0036	0.0065	0.0065	0.0065	NB	0.0036	0.0065
UE2	UE 2.1	0.0144	0.0144	0.0144	0.0144	0.0016	B	0.0144	0.0016
	UE 2.2	0.0061	0.0061	0.0061	0.0061	0.0061	B	0.0061	0.0061
UE3	UE 3.1	0.0021	0.0185	0.0103	0.0021	0.0021	B	0.0185	0.0021
	UE 3.2	0.0110	0.0110	0.0110	0.0110	0.0110	B	0.0110	0.0110
CB1	CB 1.1	0.0173	0.0173	0.0173	0.0173	0.0173	B	0.0173	0.0173
	CB 1.2	0.0139	0.0139	0.0139	0.0139	0.0139	B	0.0139	0.0139
CB2	CB 2.1	0.0131	0.0131	0.0131	0.0026	0.0131	B	0.0131	0.0026
	CB 2.2	0.0024	0.0118	0.0118	0.0118	0.0118	B	0.0118	0.0024

Also, Equations (E7) and (E8) provided separation measures from positive ideal solution (S+) and negative ideal solution (S-) for each parameter. Further, Equation (E9) calculated the parameter-wise closeness coefficient for all urban void plots as given in Table 10. The coefficient score (Ci*) was multiplied with Parameter weightage (Pw) to derive parameter-wise UVAI. At the end of Table 10, all parameter-wise indices are combined to provide a Plot-wise final UVAI indicating the potential of each void plot with its ranking which is calculated using Equation (E10).

Table 10. TOPSIS: Table showing Parameter-wise relative Closeness coefficient and ranks

Plot Code	UV1	UV2	UV3	UV4	UV5
PSP (Pw = 0.444)
Sj+	0.0042	0.0067	0.0117	0.0115	0.0114
Sj-	0.0173	0.0168	0.0134	0.0123	0.0125
Ci*	0.8049	0.7137	0.5343	0.5163	0.5217
UVAI (PSP)	0.3574	0.3169	0.2372	0.2292	0.2316
SEP (Pw = 0.178)
Sj+	0.0239	0.0218	0.0254	0.0160	0.0195
Sj-	0.0155	0.0178	0.0129	0.0183	0.0182
Ci*	0.3934	0.4495	0.3376	0.5340	0.4827
UVAI (SEP)	0.0700	0.0800	0.0601	0.0951	0.0859
UEP (Pw = 0.287)
Sj+	0.0217	0.0109	0.0130	0.0225	0.0259
Sj-	0.0183	0.0256	0.0237	0.0180	0.0126
Ci*	0.4574	0.7017	0.6451	0.4449	0.3282
UVAI (UEP)	0.1313	0.2014	0.1851	0.1277	0.0942
CBP (Pw = 0.091)
Sj+	0.0279	0.0227	0.0227	0.0290	0.0227
Sj-	0.0206	0.0227	0.0227	0.0201	0.0227
Ci*	0.4246	0.5000	0.5000	0.4095	0.5000
UVAI (CBP)	0.0386	0.0455	0.0455	0.0373	0.0455
Final UVAI (PSP+SEP+UEP+CBP)
Final UVAI	0.5973	0.6438	0.5280	0.4892	0.4573
Ranking	2	1	3	4	5

Finally, the Urban Void Plot UV2 received the highest ranking with a combined UVAI of 0.6438 and the Plot UV5 received the lowest ranking with a combined UVAI of 0.4573. Table 10 provides an ‘Ideal Solution’ derived through the TOPSIS method for the decision-makers referring to which they can focus on parameter-wise improvement of urban voids.

Ranking using the VIKOR Method for Compromised Solution

Table 11 provides the best ${f^{*}}_i$ and the worst ${f^{-}}_i$ values of all criteria and sub-criteria for all the selected voids calculated using Equation (E11) and Table 8.

Table 11. VIKOR: The Best ${f^{*}}_i$ and the worst ${f^{-}}_i$ values of all criteria and sub-criteria

Criteria	Sub-Criteria	f*i (Max)	f*- (Min)	UV1	UV2	UV3	UV4	UV5
PS1	PS 1.1	0.0704	0.0391	0.0000	0.0000	0.0270	0.0000	0.0000
	PS 1.2	0.0747	0.0415	0.0000	0.0000	0.0980	0.0000	0.0000
	PS 1.3	0.1245	0.0138	0.0000	0.0000	0.0780	0.0390	0.0390
	PS 1.4	0.083	0.0830	0.0000	0.0000	0.0000	0.0000	0.0000
PS2	PS 2.1	0.4022	0.3330	0.0486	0.0122	0.0122	0.0486	0.0486
	PS 2.2	0.0906	0.0503	0.0000	0.0000	0.0293	0.0293	0.0147
PS3	PS 3.1	0.0731	0.0731	0.0000	0.0000	0.0000	0.0000	0.0000
	PS 3.2	0.143	0.0477	0.0000	0.0487	0.0487	0.0730	0.0730
	PS 3.3	0.0885	0.0632	0.0270	0.0135	0.0135	0.0270	0.0270
PS4	PS 4.1	0.0731	0.0244	0.0000	0.0000	0.0000	0.0990	0.0990
	PS 4.2	0.0732	0.0244	0.0000	0.0000	0.0000	0.0540	0.0540
	PS 4.3	0.0703	0.0703	0.0210	0.0210	0.0210	0.0210	0.0210
	PS 4.4	0.0662	0.0662	0.0167	0.0167	0.0167	0.0167	0.0167
	PS 4.5	0.0797	0.0159	0.0175	0.0350	0.0350	0.0350	0.0350
SE1	SE 1.1	0.0782	0.0434	0.0635	0.0000	0.0000	0.0000	0.0000
	SE 1.2	0.0542	0.0108	0.1430	0.0715	0.0715	0.0715	0.0715
	SE 1.3	0.0458	0.0458	0.0525	0.0525	0.0525	0.0525	0.0525
SE2	SE 2.1	0.0911	0.0481	0.0607	0.0879	0.1207	0.0902	0.1207
	SE 2.2	0.0997	0.0527	0.0281	0.0833	0.1498	0.0632	0.1498
SE3	SE 3.1	0.0704	0.0141	0.1580	0.1580	0.1580	0.0790	0.0790
	SE 3.2	0.0806	0.0448	0.0740	0.0000	0.0740	0.0740	0.0000
UE1	UE 1.1	0.1005	0.0377	0.1155	0.0770	0.0193	0.1155	0.1155
	UE 1.2	0.0744	0.0413	0.0270	0.0540	0.0000	0.0000	0.0000
	UE 1.3	0.0562	0.0562	0.0555	0.0555	0.0555	0.0555	0.0555
	UE 1.4	0.0823	0.0457	0.0395	0.0395	0.0000	0.0000	0.0000
UE2	UE 2.1	0.081	0.0090	0.0000	0.0000	0.0000	0.0000	0.1780
	UE 2.2	0.059	0.0590	0.0515	0.0515	0.0515	0.0515	0.0515
UE3	UE 3.1	0.1296	0.0144	0.1430	0.0000	0.0715	0.1430	0.1430
	UE 3.2	0.0731	0.0731	0.0000	0.0000	0.0000	0.0000	0.0000
CB1	CB 1.1	0.0558	0.0558	0.1550	0.1550	0.1550	0.1550	0.1550
	CB 1.2	0.0514	0.0514	0.1355	0.1355	0.1355	0.1355	0.1355
CB2	CB 2.1	0.0684	0.0137	0.0955	0.0955	0.0955	0.1910	0.0955
	CB 2.2	0.0517	0.0103	0.2280	0.1140	0.1140	0.1140	0.1140

Table 11 values and Equations (E12), (E13), (E14), (E15) provide the values for ${(S}_j)$ and ${(R}_j)$ ultimately giving the values of $Q_j$ as given in Table 12. These $Q_j$ values were multiplied with Parameter weightage (Pw) to generate the UVAI. Lastly, all parameter-wise UVAI were combined and ranked from the lowest to the highest score in an ascending manner providing the final ranking of Urban Voids using the VIKOR method giving a ‘Compromised Solution’.

Table 12. VIKOR: Table showing Parameter-wise Compromised solution and Ranks

Plot Code	UV1	UV2	UV3	UV4	UV5
PSP (Pw = 0.444)
Sj	0.1308	0.1470	0.3793	0.4426	0.4279
Rj	0.0486	0.0487	0.0980	0.0990	0.0990
Qj	-0.3124	-0.2699	0.6737	0.8454	0.8072
UVAI (PSP)	-0.1387	-0.1198	0.2991	0.3753	0.3584
SEP (Pw = 0.178)
Sj	0.5798	0.4533	0.6265	0.4305	0.4735
Rj	0.1580	0.1580	0.1580	0.0902	0.1498
Qj	1.0524	0.8061	1.1432	-0.3313	0.7131
UVAI (SEP)	0.1873	0.1435	0.2035	-0.0590	0.1269
UEP (Pw = 0.287)
Sj	0.4320	0.2775	0.1978	0.3655	0.5435
Rj	0.1430	0.0770	0.0715	0.1430	0.1780
Qj	0.1477	-0.6705	-0.8796	0.0075	0.6438
UVAI (UEP)	0.0424	-0.1924	-0.2524	0.0022	0.1848
CBP (Pw = 0.091)
Sj	0.6140	0.5000	0.5000	0.5955	0.5000
Rj	0.2280	0.1550	0.1550	0.1910	0.1550
Qj	0.4630	0.0000	0.0000	0.3067	0.0000
UVAI (CBP)	0.0421	0.0000	0.0000	0.0279	0.0000
Final UVAI (PSP+SEP+UEP+CBP)
UVAI	0.1331	-0.1688	0.2501	0.3464	0.6701
Ranking	2	1	3	4	5

Using Equation (E16), the Urban Void Plot UV2 was ranked the highest with a compromised score of -0.1688 and the Plot UV5 was ranked the lowest with a compromised score of 0.6701 thus providing a logical path for decision makers to decide the priority of voids for further improvement.

Ranking using MOORA Method for Optimized Solution

Using NDM values given in Table 8 and WNDM values given in Table 9 along with beneficial and non-beneficial criteria, the calculations for the MOORA method were performed using Equations (E17) and (E18).

Table 13. MOORA: Composite score for urban voids and their ranks

Plot Code	UV1			UV2	UV3	UV4	UV5
PSP (Pw = 0.444)
Xij * Wij (B)			0.1241	0.1296	0.1142	0.1014	0.1023
Xij * Wij (NB)			0.0000	0.0000	0.0000	0.0000	0.0000
yi			0.1241	0.1296	0.1142	0.1014	0.1023
UVAI (PSP)			0.0551	0.0575	0.0507	0.0450	0.0454
SEP (Pw = 0.178)
Xij * Wij (B)			0.0514	0.0607	0.0475	0.0656	0.0617
Xij * Wij (NB)			0.0000	0.0000	0.0000	0.0000	0.0000
yi			0.0514	0.0607	0.0475	0.0656	0.0617
UVAI (SEP)			0.0091	0.0108	0.0085	0.0117	0.0110
UEP (Pw = 0.287)
Xij * Wij (B)			0.0394	0.0597	0.0573	0.0394	0.0266
Xij * Wij (NB)			0.0145	0.0132	0.0188	0.0188	0.0188
yi			0.0249	0.0465	0.0385	0.0206	0.0078
UVAI (UEP)			0.0071	0.0134	0.0111	0.0059	0.0022
CBP (Pw = 0.091)
Xij * Wij (B)			0.0467	0.0561	0.0561	0.0456	0.0561
Xij * Wij (NB)			0.0000	0.0000	0.0000	0.0000	0.0000
yi			0.0467	0.0561	0.0561	0.0456	0.0561
UVAI (CBP)		0.0042		0.0051	0.0051	0.0042	0.0051
Final UVAI (PSP+SEP+UEP+CBP)
UVAI	0.0756			0.0868	0.0753	0.0668	0.0638
Ranking	2			1	3	4	5

Equations (E19) and (E20) provided the composite score $y_{\mathrm{\text{i}}}$ for void plots using all Inventory criteria. The Parameter-wise UVAI was calculated for all the void plots by multiplying the Parameter weightage (Pw). The combined UVAI is mentioned in Table 13. These scores were ranked in a descending order to get the desired prioritization giving a ‘Optimized Solution’ for the decision-makers. The results indicated that UV2 was ranked the highest with a UVAI of 0.0868 and UV5 ranked the lowest with a UVAI of 0.0638.

Establishing correlations between Ranking of all methods

The research methodology ascertained the need to check the correlation between the ranking of voids derived from the three prominent methods was calculated by Equation (E21) which resulted in a perfect positive correlation with a score of 1 among all of them, as shown in Table 14.

Table 14. Spearman’s Rank Correlation Results

	A. Correlation between MOORA and VIKOR Method Ranks
Rank (TOPSIS)		Rank (VIKOR)	Rank (MOORA)	d	d2	Formula	Result
2		2	2	0	0	$$\Sigma d^2$$	0
1		1	1	0	0	$$6\Sigma d^2$$	0
3		3	3	0	0	$$n(n^2-1)$$	120
4		4	4	0	0	$$\frac{6\Sigma d^2}{n(n^2-1)}$$	0
5		5	5	0	0	$𝛒$	1

Discussion on The Implications of UVAI for Decision-Making

The review of literature pointed to the need for a ‘Methodical Framework’ for multi-criteria assessment for knowing the development potential of voids which has been attempted in a limited manner. It also informed the deficiency of rational and methodical decision-making processes for their ‘Prioritization’ using these development potential scores.

Hence, the research cumulatively tried to fill these vital gaps by developing a robust ‘Inventory’ for assessing the development potentials through thoughtfully curated and relevant parameters, criteria and sub-criteria ultimately generating the Urban Void Analysis Index (UVAI). The comprehensive inventory consisted of the context-specific combination of both quantitative and qualitative vital parameters.

Finally, the results provided fair ranking using a combination of different Hybrid MCDM methods mainly the AHP used in combination with TOPSIS, VIKOR, and MOORA. It yielded the prioritization which was validated through a comparative correlational model and all combinations produced similar final results. Further, it offered superior choices for decision makers whereby they could flexibly choose any combination suitable for their context in an ideal way, compromised way or optimized way which became the strength of this study.

Although, the present research considered a limited assessment for the fewer number of voids at the zone-level, the higher number of voids, category-wise distribution patterns, changes in government policies, new technology, and external influencers may have an impetuous impact on the methodology. Also, more number of surveys per void plot could yield better results.

Overall, the logical interpretation of results informed the need of specific policy recommendations. First, to reduce the conflicts of Administration, the ULB need to set up the Special Purpose Vehicle (SPV) for coordinating between multiple authorities, carry out Inventory-based surveys and resolve planning issues using the UVAI scores. Secondly, it suggests to strengthen the municipal finances by creating Land Bank of urban voids and utilize them as per the derived Prioritization. Thirdly, it recommends to revise all the Leases and contracts as per the prevalent market rates using the UVAI ranking. Lastly, it also conveys the logical Distribution of the available assets, financial, and human resources as per the derived urban void rankings across the administrative zone.

Cumulatively, the research objectives were achieved by this novel, unbiased, and scientific approach. The issues of Micro-revitalization of urban voids have been tackled qualitatively by most of the researchers, however, this research focuses on both qualitative and quantitative aspects of void spaces. Such hybrid approach has been attempted for the first time especially in the Indian context.

Ultimately, the outcomes of the study can widely be applicable due to its inbuilt flexibility and adaptability. It will benefit decision-makers in the logical selection of urban voids for the development and the effective distribution of confined valuable resources to meet the demands of the citizens with accuracy.

Conclusions

Land is a precious matter when it comes to urban planning in densely populated cities of the Global South. It is pertinent to tackle the precarious issue of redevelopment of urban voids, especially in compact Indian cities having plenty of micro-scaled voids whose benefits could be tapped by prioritizing these resources. Internationally, a lot of efforts have been made to tackle the urban voids, however, they were constrained to their qualitative understanding performed using limited parameters. Hence, for bringing urban voids into mainstream development, the revival of urban voids becomes the priority.

To achieve this goal, the process of selection of such plots amongst the thousands of them needs to be impartial. Therefore, this research significantly contributed to the meticulous investigation of the hidden potentials of void plots employing the eight-step interconnected methodology. It also provided a comprehensive and robust ‘Inventory’ for the subsequent multi-layered analysis of void spaces which ultimately generated an Urban Void Analysis Index (UVAI) at the zone level achieved after uncovering the ground data.

Therefore, the robustness of such an approach provided an informed and structured framework ensuring reliability and precision in the decision-making, which was hardly attempted in the domain of the relevant research. The UVAI stands as an impartial, pragmatic and scientific tool that can drive government decisions for the reasonable allocation of financial and human resources through their machinery.

Additionally, the research recommends the integration of UVAI and the incorporation of hybrid MCDM methods into multi-level urban planning administrative frameworks. The strategic revitalization of intrinsic urban voids with detailed empirical data could be carried out at the Ward level, Zone Level, Neighbourhood Level or City level by establishing a special unit under the Town Planning Department of ULB. The multi-dimensional analysis could be strengthened by incorporating the stakeholders preferences, capacity building and training programme for empowering their analytical decision-making. Enhancing the bottom-up approach of planning through community engagement and participatory planning needs to be encouraged.

The research could be extended further by scaling it up to different levels of cities and regions by collaborating with several levels of government. There is a good scope to standardize methods of data collection and processing with advanced GIS and AI-based technological integrations for big data analysis. The exploration of an interdisciplinary approach in micro-scaled redevelopment and its evaluation for long-term impacts on urban sustainability, liveability and resilience could be an area for further research.

Successively, the overall research process of Micro-scaled urban void revitalization will contribute to improving the urban realm bringing more sustainable and catalytic solutions for city development.

Author Contributions

Conceptualization, H.R.; methodology, H.R., T.V., A.P.; investigation, H.R.; resources, H.R.; data curation, H.R., T.V., A.P.; writing—original draft preparation, H.R.; writing—review and editing, H.R., T.V., A.P.; supervision, T.V., A.P. All the authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

The authors would like to acknowledge Amity University, Jaipur, Rajasthan for their support in the completion of this research study.

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