Spatial-temporal patterns of self-organization: A dynamic 4D model for redeveloping the post-zoning city

Abstract

Time is the main axis for understanding the functional, economic, and social aspects of self-organized redevelopment. When such processes are intensive and are conducted contemporaneously by large numbers of urban agents on different spatial and temporal scales and as a result of different motivations, urban planning is fragmented into multiple simultaneous and unexpected projects. The post-zoning era in urban planning stemmed from a recognition of this kind of complexity of urban dynamics and the need for a flexible planning system. Web-based geographic information systems (GIS) and planning support systems (PSS) are employed widely as digital tools to support planning practices. Still, the solutions tend to be isolated implementations that do not achieve sophisticated management of the complex temporal-spatial urban dynamics of self-organization. To this end, the article presents a useful set of multidimensional (2D, 3D, and 4D) planning tools that can be implemented by municipal planning departments to improve planning practices with relative ease. This toolbox facilitates the real-time updating of changes to individual buildings and allows all parties to see where delays are occurring, where they are impacting one another, and where environments of accelerated development are evolving in nearby urban plots. Identifying redevelopment clusters enables the formulation of an urban time-based planning policy. Using a spatial-temporal toolbox for planning, we argue, can facilitate recognition of the potential of self-organization as the leading form of contemporary urban planning.

Keywords

Comprehensive planning self-organization urban redevelopment 4D modelling space-time GIS planning support system online participatory tools Israel

Introduction

In the information era and the age of rapid urbanization, urban planning is increasingly coming to focus on nurturing economic vitality through processes of self-organization (Rantanen and Rajaniemi, 2020). That is to say, profitable initiatives for spatial interventions that originate within civil society itself (Boonstra and Boelens, 2011) play a role in economic redevelopment. These processes have helped bring about increasing recognition of bottom-up urban redevelopment as an activity that occurs within a complex and multi-centric field (i.e. concurrently in many independent plots in the city), contrary to comprehensive top-down development.

Although urban planning shapes spatial outcomes, it also encompasses issues of time, first and foremost because it is future-oriented by definition (Myers and Kitsuse, 2000). For example, even planning concepts themselves – such as rapid urbanization, accelerated development, urban redevelopment, and building preservation – incorporate temporal terms. Particularly in urban redevelopment, time is a central element of understanding the functional, economic, and social aspects of the constantly changing city. Since self-organization plans progress concurrently, but at different rates, they tend to always be in different stages of planning, authorization, demolition, and construction. The management of such self-organizing plans should include a significant component of time.

In my capacity as director of the Strategic Planning Department of a local municipality (note: Daphna Levine was the director of the Strategic Planning Department of Bat Yam municipality in the years 2012–2018.) that is currently witnessing intensive self-organized redevelopment activity, I have on numerous occasions encountered the issue of planning system management vis-à-vis urban infrastructure. For example, the local water corporation, which is responsible for responding to urban growth, attempts to predict where and when it will need to expand its pipe network. However, as it is not known whether each self-organized plan will ultimately be implemented, water corporations need to constantly monitor the work of planning departments and vice versa. Despite the standard perception that planning proceeds detached from the stages of implementation, the two processes are inseparable in urban practice, particularly in an environment of multiple initiatives for spatial intervention. Both depend on effective management of the multi-centric vector of time in city redevelopment.

Graham and Healey (1999), and many others following them, have proposed rethinking the planning of urban regeneration using a spatial conception of the city as dynamic relations and processes. Specifically, they have assessed the many spatial and temporal experiences that occur contemporaneously and recognized the continuous negotiation engaged in by large numbers of urban agents using mutual communication and different interpretation on different spatial and temporal scales (McCann, 2003).

The post-zoning era in urban planning stemmed from a recognition of this kind of complex urban dynamics. It is based on the need to replace the rigid division of land designations with a new set of tools, as well as the demand for a flexible planning system instead of the traditional custodian of zoning (Rantanen and Rajaniemi, 2020). Ideas such as long-term planning, major plans, and comprehensive planning are perceived as anachronistic and no longer fit for the purpose (Laurian and Inch, 2019).

The challenge facing current urban planning, therefore, lies in addressing the dynamic of multiple time axes, and particularly initiatives aimed at redeveloping the urban fabric that originate in civil society itself (Boonstra and Boelens, 2011). If self-organized initiatives are a major engine for redevelopment, then co-operative or participatory tools that enable the numerous simultaneous initiatives to be conducted should be at the forefront of the planning systems. Although web-based geographic information systems (GIS) and planning support systems (PSS) are widely adopted as digital tools to support planning practices (Degkwitz et al., 2021), the solutions still tend to be isolated implementations that do not achieve sophisticated management of the complex temporal-spatial urban dynamics of self-organization. In practice, the tools used by the planning departments of local municipalities to manage the plans are not necessarily the most suitable for the job (Geertman and Stillwell, 2020). Apparently, this stems from the desire of planners to maintain control in their hands, in addition to the technological gap (Parry and Bithell, 2012; Scheutz and Harris, 2012).

This article, therefore, is an attempt to advance a prototype of a multidimensional (2D, 3D, and 4D) online toolbox that responds to the space-time challenges of multiple self-organization planning. From a spatial perspective, we are responding to the reality of fragmented redevelopment using bottom-up organization. From a temporal perspective, we are responding to the simultaneous time of redevelopment, which exists in the course of urban dynamics.

Our project employs Esri's ArcGIS Online and QGIS platforms and offers a set of tools that can efficiently leverage the existing GIS data to benefit urban planning practice and improved collaboration with program initiators from civil society. Here, we would like to stress that our project is not a fully developed PSS, and that further research is required in this direction. Such development could enable planners and other municipal departments to overcome technological obstacles relatively easily, without the need for programming knowledge, and to contribute to advanced, fit-for-purpose city information modelling. Currently, the simulation enables us to see where delays are occurring, where they are impacting one another, and where environments of accelerated redevelopment are taking form. It enables planners to formulate balanced policy for public needs and to coordinate among the required public infrastructures in a manner that considers the cumulative impact of occurrences in the city. And finally, it enables anyone to follow the routes of redevelopment and to examine expectations regarding implementation.

Our case study model is based on data regarding all the plans and permits that were present in the study area's municipal systems in 2019. As the arena of research, we selected a neighborhood in the city of Bat Yam (Israel) that is currently undergoing massive redevelopment in response to the urgent national goal of increasing housing supply.

The article begins by presenting a theoretical framework for urban planning from zoning to post-zoning, based on a discussion of the temporal dimension of self-organization and spot-zoning. It then shows how planning-support tools are needed to manage the urban dynamic when it evolves from the bottom up. In the methodological section, we show how spatial and temporal patterns can be interwoven into a dynamic 4D system with a user-interface. In addition, we present a heat map-based analysis of the formats of redevelopment over time. Finally, using the advanced multidimensional toolbox, we show how future studies can lead to a recognition of self-organization as a force that drives advanced urban planning and that facilitates positive interaction between citizen initiatives and city planning and management.

A space-time framework for self-organization in the post-zoning era

Many researchers argue that contemporary cities are based on complex, dynamic systems of relationships (Moroni et al., 2018; Portugali, 2016; Portugali et al., 2012). Urban aggregations contain large numbers of simultaneous and mutually influential agents, objects, and actions (Farías, 2010). The urban study draws inspiration from the research on the sociology of time (Lefebvre, 2004) and from the geographical work that combines temporal dimensions with concepts such as traffic studies and time schedules of facilities, while focusing on everyday behaviors on the individual and the city scales (Amin and Thrift, 2002). Some point to time as a major attribute of contemporary cities, referring to the cities of today as “cities of time” (Charbgoo and Mareggi, 2020, quoting Henckel et al., 2013).

However, although time appears to be an inseparable part of all planning processes, it still lacks an explicit and sweeping presence in the planning practice tools (Charbgoo and Mareggi, 2020), especially as responsive and interconnected planning processes (Potts, 2020) in real time.

There is an historical reason for prioritizing space over the temporal dimensions of planning systems. Until a few decades ago, the prevailing approach was the modern conception of Euclidean time as homogenous and linear, which allows it to serve as a measure of spatial change (Graham and Healey, 1999). This conception of time created the conditions that made urban planning possible, in its perception of the future as development toward progress that can be actualized using rational tools (Connell, 2009). The modernist division between defined land-use goals and realms was contingent upon the perception of the future as “abstract, empty and ready to be exploited” (Laurian and Inch, 2019: 270) and upon the ability to define spaces as closed and static systems (Alfasi, 2018).

Postmodern time, on the other hand, can be characterized as a collapse of the time-space continuum. Deleuze proposes perceiving time as an incalculable number of syntheses and interactions for which time is not a continuous flow but rather an interconnected combination of unique chaotic events (Deleuze, 1994). Postmodern planning paradigms reflect this conception of time in their recognition of planning as ongoing, socially interactive, and non-linear development (Healey, 2011). This approach, supported by relational theories of urban time-space, dynamic conceptualizations of “multiplex” places and cities (Graham and Healey, 1999), and theories of social agency (Latour, 2013), has led to a loss of faith in our ability to predict and plan the future of cities as a whole. Cities are understood as spaces with a large number of dynamically connected parts, including an abundance of exchanges of information, people, energy, and matter (Partanen and Wallin, 2017; Portugali, 1997). The consequence of this heightened urban and social complexity, in Potts's words (Potts, 2020: 280), is “reduced predictability of urban processes, reduced efficacy of traditional planning methodologies in addressing urban issues, and increased periods of chaotic change” (Partanen, 2018).

In urban planning, self-organization is narrowly defined as initiatives aimed at spatial interventions originating in civil society itself, through autonomous community networks of citizens located outside government control (Boonstra and Boelens, 2011). A broader definition pertains to the array of residents, planning institutions, city environments, and mutually related planning tools and products (Partanen and Wallin, 2017; Rantanen and Faehnle, 2017). Under the rubric of self-organization, we find realms such as informal planning, insurgent planning, active urban citizenship, and bottom-up participation in the planning system (Eizenberg, 2019).

In complexity theory, self-organization refers to the limitations on the individual actor's ability to steer the process stemming from the autonomy of other actors and their ability to conduct and organize themselves as they choose (Boonstra and Boelens, 2011). Complexity relies on self-organization, which involves the reorganization of components within the system to produce order, despite the lack of guidance from outside the system (Rantanen and Faehnle, 2017). Inspired by Mouffe's (2000) theory of agonistic pluralism, we can say that self-organization is an expression of conflicting interests and values, all of which have the potential to take part in shaping different future pictures of urban space.

In the realm of public participation in planning, self-organization is considered to be the most far-reaching level of citizen–local government relations. It is ranked above information sharing – unidirectional communication between government and citizens, and an interaction that means dialogue and feedback between the parties – and co-production, in which the public sector and the citizenry take advantage of each other's assets and resources of one another in order to achieve better and more effective results (Falco and Kleinhans, 2018). In other words, self-organization is the stage of conflicting interests and values, and multiple actions and actors, in which social interactives take place on the urban space. From this perspective, self-organization is an alternative medium in which citizens share information and independently organize to promote their interests.

According to the literature, the pretension of the hierarchal planning system and statutory land-use plans to regulate and coordinate relationships and priorities among the numerous private parties operating on the city level is impossible when it comes to self-organization (Alfasi, 2018). This spontaneous and complex kind of urban transformation is neither linear in essence nor predictable and therefore cannot be managed or planned using traditional spatial tools (Portugali, 2011).

The concept of “post-zoning” is not widespread and lacks an agreed-upon definition (Been and Infranca, 2013). Literally, however, it refers to the replacement of the division of land into zones with different land uses with a completely new and flexible set of tools for urban planning (Rantanen and Rajaniemi, 2020). In the post-zoning era, urban planning strives to be capable of perceiving phenomena without reducing their complexity. It must be able to provide solutions for complex operative environments with multiple agents operating simultaneously (Rantanen and Rajaniemi, 2020). At the same time, urban planning needs to respond to the volume, velocity, and relational nature of the data currently being produced in a data-rich urban context (Potts, 2020).

In the post-zoning era, the planning theories suggest different frameworks of qualitative codes and abstract rules to suit the complexity of the built environment and promote the development of spontaneous spatial order (Moroni, 2015). These principles are meant to contend with a variety of unknown situations, indicating desired development but leaving spatial self-organization free to operate (Portugali et al., 2012). Nonetheless, on the practical level, the world of planning is currently in a hybrid state.

The dynamics of self-organization have resulted in the increased use of informal planning tools, private–public contracts, and non-statutory strategic processes in many countries. These tools are indicative of a lack of coherency in the planning system regarding the actual behavior of socio-spatial systems (Rantanen and Rajaniemi, 2020). Comprehensive city plans have not been removed from the drafting table altogether, certainly not in developing countries in which the state is involved in stimulating the processes of development. The result is a mismatch between the “system of rules” and the “order of actions” (Moroni, 2010: 139).

From the perspective of time, land-use planning determines the spatial details before a concrete initiative is implemented and is therefore not well suited to the changing circumstances of site-specific initiatives (Alfasi, 2018). As planning and approving such plans takes too long due to the rapidity of developments on the ground (Padeiro, 2016), when they are finally approved, they are often discovered to be irrelevant or not implementable (Loh, 2011). In this way, urban redevelopment is currently enacted in a manner that does not correspond to the planning (Laurian and Inch, 2019), and the outcome is a “planning blight.”

Accordingly, plans for the long term are bypassed by developments and projects, each based on its own merits and circumstances. City and regional planning departments employ the practice of site-specific local revisions to comprehensive zoning plans. The essence of this practice, known as “rezoning,” “spot-zoning” (Buitelaar and Sorel, 2010), and, on occasion, “up-zoning” or “down-zoning” (Hills and Schleicher, 2011), is the introduction of local change to the overall planning directives pertaining to a single element or a limited, defined area. In practical terms, then, the current work of the planning system boils down to the ongoing local revision of comprehensive plans (Alfasi, 2006; Berry, 2001; Buitelaar et al., 2011; Buitelaar and Sorel, 2010; Charney, 2013; Waldner, 2009).

The academic criticism highlights the weakness of site-specific plans stemming from their unsuitability for, and at times contradiction to, the provisions of comprehensive planning. Spot-zoning plans are liable to be motivated by site-specific judgement calls without consideration of their broader impact (Alfasi, 2018), leading some researchers to argue that they are not even worthy of being designated as “planning” (Alexander, 2016). This raises not only an existential question regarding the very essence of planning but also the normative question of whether we should be planning at all (Purcell, 2013).

In contrast to this approach, some have argued that developing digital technologies provide new opportunities for the emergence of self-organization as more transparent, interactive, user-driven, and “co-operative” planning (Rantanen and Faehnle, 2017). From this perspective, urban complexity can be perceived as an asset as opposed to a loss of direction (Rantanen and Rajaniemi, 2020). We concur with this approach and posit that the criticism of planning fragmentation stems from the fact that the existing technological tools for sophisticated management and monitoring of this complex urban dynamic, driven by civic initiatives, are still not widely used (Geertman and Stillwell, 2020). The solutions of digital tools to support planning practices tend to be isolated implementations aimed at a single planning purpose due to the specific requirements concerning their data, methodology, involved stakeholders, etc. (Degkwitz et al., 2021).

Research using space-time GIS simulations and applications has grown tremendously over the past several decades (Wang et al., 2019). Integrating time into GIS provides better understanding of changes of geographic information in terms of morphology, topology, and attributes, as well as patterns, processes, and trends (Nara, 2017). Space-time GIS embraces spatial and temporal data through the processes of conceptualization, representation, computation, and visualization (Wang et al., 2019). Spatiotemporal simulation models represent decision alternatives (Feng, 2017), and their consequences can be represented, analyzed, and visualized to support the decision-making process of policy makers using what-if scenario analysis (O’Sullivan and Perry, 2013).

Unlike GIS systems, which are general-purpose tools for the storage, analysis, and manipulation of spatial data, PSS are designed to support efficient planning practices and decision making (Batty, 2007). They are therefore a set of methods that are accessible as part of an interactive user interface (Geertman and Stillwell, 2009). Accordingly, the existing relevant planning platforms, including, for example, CityPlanner™, CommunityViz Timescope, and ArcGIS (Glaas et al., 2020) offer all or some of the applications: a planning support system, 3D visualization, time series GIS tools, and public participatory GIS. However, the literature indicates a lack of implementation of 4D modelling that includes more than one plan at a time, whereas what is currently needed is the ability to illustrate diverse urban processes of change as un-orchestrated interactions occurring simultaneously in many different fields in the city (Johansson et al., 2012). In addition, the modeling should be applicable and simple to use for non-programmer urban planners, based on existing GIS data.

Despite the estimated benefits of spatial-temporal simulations (Feng, 2017), the expertise associated with the use of high technological performance has been recognized as hindering their use and development by non-programmers (Parry and Bithell, 2012; Scheutz and Harris, 2012). In this context, we should also look at the efforts to implement online participatory tools (OPT), known also as digital participatory platforms (DPP). They are part of the trend of digitization of city data through the 3D representation of cities, which is usually accompanied by a model for civic participation (Glaas et al., 2020). Despite the marked increase in the use of OPTs (Afzalan et al., 2017) and the known advantages of the tools (Georgiadou and Stoter, 2010), citizen participation in planning has not increased in any meaningful way in recent years (Hjerpe et al., 2018) and is frequently criticized for its inadequate implementation. Studies show that the reasons for this are found not in the technical functioning but rather in the lack of a city strategy for meaningful public participation, as well as disagreement regarding the role of citizen contribution (Glaas et al., 2020). Policy makers and planners are in no hurry to include the perspectives and positions of residents, as this can introduce uncertainty to the planning in practice (Blunkell, 2017).

Public participatory Geographical Information Systems (PPGIS) is an example of a concept that supports collaborative urban planning. The web-based two-way exchange of plans facilitates both broad public access and the sharing of ideas (Johansson et al., 2012). The use of space-time (4D) visualization with public involvement is a relatively new application. It is based on 3D models that provide visual representation of aspects of time and timing (Stellingwerff and Kuhk, 2004) and involves the usage of simulation of construction plans, including future scenarios (Hartmann et al., 2008; Jongeling and Olofsson, 2007).

In the case of urban processes in which entire areas undergo re-planning, both the public on the one hand, and planners and decision makers on the other hand, need a clear, real-time visual image of the current and future status of all activities in the city. Such visual information can constitute an important asset for a successful process of citizen participation that enables them to follow the development of their city over time (Lafrance et al., 2019), especially if they are the initiators of self-organizing programs.

At our research site, the city planning system is collapsing under the sheer abundance of individual projects, which engage in constant ongoing negotiations regarding the possible futures of the urban space via spot-plans for expansion or demolition and the construction of residential buildings. Homeowners lead most of the planning work itself, in inherent cooperation with developers. Therefore, we argue that an advanced platform that presents many kinds of urban plans of different statuses (Johansson et al., 2012) is required for municipal management that coordinates the basis for communication with the public regarding ongoing urban changes. Most notably, it is essential for allowing the public planning system to transition from leading to enabling (Steen Møller and Stahl Olafsson, 2018).

Research area and methodology

The research area: Bat Yam

The city of Bat Yam was selected as the study's research area due to its nature as a metonym of the problems and the hopes of urban crowding. The city is characterized by an absence of land reserves, old and neglected buildings, a socioeconomically disadvantaged population, and an impoverished municipal treasury (Eizenberg and Cohen, 2015). At the same time, its profit potential places it squarely on the real estate market. As a result, both the residents of the city and the local municipality have already been engaged in the turmoil of site-specific entrepreneurial planning for approximately a decade and, more than in any other city in Israel, are leading the way in this respect (Boso, 2019).

Bat Yam is located on the Mediterranean coastal plain, in the first ring of the Tel Aviv metropolitan area (see Figure 1). It has an area of 816 hectares and its population stands at 129,000 (CBS, 2020). For the sake of comparison, Bat Yam is almost twice as densely populated as Tel Aviv. According to Israel's Central Bureau of Statistics, Bat Yam is ranked lowest in quality of urban life and highest in population density out of all the cities in Israel.

Figure 1.

Map of the research area.

Figure 2.

A methodological scheme.

If all the site-specific plans for urban regeneration that have been advanced are ultimately implemented, some 26,000 residential units will be added to the city's 50,000 already standing residential units, and the population will increase by 50%. In the absence of vacant land, this means that the population density will also increase accordingly. Each year in Bat Yam, in addition to the spot zoning plans, the construction of hundreds of residential units is approved through building permits issued under National Outline Plan 38 (“TAMA 38,” as it is referred to in Hebrew); these permits apply to buildings that were built prior to 1980, which account for some two-thirds of the buildings in Bat Yam. TAMA 38 is a nationwide building plan that spearheads a strategy for urban regeneration based on upgrading, strengthening, and densifying existing housing in Israeli cities (Persov and Carmon, 2015). The plan encourages urban regeneration on the scale of the individual building by offering incentives to contractors to fund the reinforcement of residential buildings to withstand an earthquake (Margalit and Mualam, 2020). It also facilitates the addition of residential units to a building without changing the city plan, without the need to provide land for public use, and without paying an improvement tax (Feitelson, 2018; Margalit and Vertes, 2015).

Methodology

We have found the processing of data on a time axis and compatibility with an internet GIS system with temporal capacities to be the most suitable approach for a dynamic model for studying some simulations of complex self-organization processes. Also advantageous is its ease of use and implementation by planning departments working with the GIS platform.

Our multidimensional toolbox was developed in six stages (see Figure 2). The first stage involved collecting the data for all the buildings undergoing planning in the research area. In the second stage, we set parameters for the work on spatial patterns, and in the third stage, we analyzed the temporal patterns emerging from the data. In the fourth stage, we established a geographical foundation of data, including the super-positioning of several entities. To better understand the changes over time, we added independent temporal capabilities for every spatial entity using QGIS software. In the fifth stage, we published a feature layer to ArcGIS Online with the time-series enabled, and we produced a web map that incorporates the passage of time. Using this platform, we worked with space-time web applications to create an interface that is both user friendly and updatable. As it was not possible to read the vector of time into the 3D model, we created it separately in a mixed ArcGIS/QGIS environment. In the sixth and final stage, we used QGIS heat maps to perform analyses examining the patterns of temporal territories that can be expected to take form in the city.

Data collection

As a first step, we processed detailed data that was provided by the municipality of Bat Yam in mid-2019. The files we received contained vector geographical information in shapefile format and data tables pertaining to all the residential units and households, which are used by the local municipality to levy property tax. In addition, we were provided with planning and permit information from the engineering department, which contained detailed data regarding site-specific plans and permit requests, including the dates of every stage of the process, from a request's submission to its approval. We were also provided with additional information regarding urban plans by the open data system of the Planning Administration, including all the plans' documents and the appendices for building.

During the collection of data from the municipality, we encountered various problems, including: the chaotic state of the data pertaining to the existing buildings, overlapping addresses, multiple entrances, etc. We also encountered problems with the plan documents we collected from the website of the Building Administration, as data concerning the quantity and the height and format of the planned buildings are not reflected in the statistical tables of the plans but rather only in the building appendices. This data also sometimes appears in a few alternative forms in order to enable flexibility.

Spatial patterns

During the second stage of building the model, we focused on three statistical areas in northwestern Bat Yam, working with data for each building with an active file in the local system. We divided the planned buildings into the three development typologies of urban regeneration in Israel:

Housing regeneration, referred to as “Raze and Rebuild” (RR), which is the most significant typology in terms of the scope of growth in the number of residential units. This format often results in the demolition of several buildings, the merger of lots, and their use for the construction of high rises (see Figure 4). Within the city system, Raze and Rebuild projects are conducted via the planning permit track (in Hebrew, “TABA”).

The other two typologies for urban regeneration are enacted using the building permit under National Outline Plan 38, which facilitates the addition of new stories to buildings without changing the parcelization:

b. The Addition (A) Track – Expansion and additions to the volume of the original building (TAMA 38/1).

c. The Reconstruction (R) Track – Full demolition of the existing residential building and replacement with a larger structure (TAMA 38/2).

The relevant data was collected both for the present and for the situation that can be expected after the plans are executed. The building was mapped before and after according to three parameters: number of buildings, number of residential units, and building height. For the sake of the 3D model, we also reviewed all of the plans' building appendices, and we coded aspects of location in the lot, building lines and form, and land cover.

We found that the three statistical areas we coded currently contain 385 buildings, for a total of 5588 residential units. The plans apply to 71 buildings, accounting for 18% of the buildings in the research area. These buildings contain 1226 residential units, which will increase to 3902 after housing regeneration. In Table 1, the data is organized by typology.

Table 1.

Changes regarding the three typologies of housing regeneration in the study area.

	Addition			Reconstruction			Raze and Rebuild
	Before	After	Factor	Before	After	Factor	Before	After	Factor
Buildings (total)	21	21	1	6	6	1	44	23	0.5
Units (total)	432	726	1.7	63	215	3.4	731	2961	4
Units per building (avg.)	21	35	1.7	11	36	3.3	17	129	7.6
Stories (avg.)	3	6	2	2	10	5	3	24	8

Temporal patterns

In the third stage, we conducted a statistical study of the lifecycle of each redevelopment project. Based on real-life city-wide data, we determined the average duration of every planning stage, from the plan's approval to the issuing of a building permit, and the duration of the project's building stage. We characterized the changes that each building would undergo on the axis of time within the event table, including structural attributes and the time of onset and conclusion of each stage.

The city-wide statistical findings indicate that, in Bat Yam, the stage of plan approval lasts approximately 24 months in the Local Committee and another 29 months in the Regional Committee. Once the plan is approved, work can begin on the building permit stage, which lasts an average of 31 months. The duration of the construction depends on the scope of the construction of each typology: Additions (A) take an average of 18 months, Reconstruction projects (R) take 24 months, and Raze and Rebuild projects (R&R) take an average of approximately 36 months. Figure 3(a) presents the relationship between the typologies and the average duration of each stage (in months).

Figure 3.

Temporal patterns of redevelopment in the study area.

But in an area undergoing redevelopment through site-specific plans, each building undergoes the process at a different pace and a different time, according to when the process began, and the delays encountered along the way. The duration of the process is not always consistent with the statistics, as it depends on the specific attributes of the existing and the planned building. We therefore read into each model the real-life data contained in the data files that we received, and we used the statistical information only to better develop the expectations for future stages. Figure 3(b) reflects the projected implementation of every typology for each year in terms of the number of expected new stories. Due to the extended planning and the broad scope of building carried out in R&R projects, their implementation began later and will continue for several years after the completion of all the A and R projects. Although additional A and R processes may have been initiated during this period, the city system currently holds no details about them.

Space-time geo-database

In the fourth stage, we examined several alternatives for the creation of a visual analysis of building changes at all the sites along the axis of time. For each project, it was important for us to maintain a time axis that was independent of its surroundings, as well as the ability to easily change and update the attributes of the building and possible time delays. We selected an approach that displays changes over time using vector layers, established on the principle of entity-based representation of spatial-temporal plans. In this method, every event is read into the system through the duplication of the geographical entity and change to the geometric characteristics, in accordance with the beginning and ending time stamp. The result is “stacked super-positioning,” in which all the events are displayed in the same GIS layer and for each choice. Using a time-range scale, one can filter the relevant entity out of the stack.

Space-time GIS web application

In the fifth stage, we worked with Esri ArcGIS Online software due to its ability to sort using a two-way time axis (beginning and end).The time axis facilitated the addition of animation capabilities by representing change in space as frames of specific points in time and within time periods . In addition, the user-friendly processes provided us with sufficient tools of analysis, making the writing of code unnecessary.

We adapted the geographical-temporal layer we created to ArcGIS Online. We then published a feature layer and defined it as time enabled – meaning that it allows sorting by time. The result was an internet map that displayed the change in urban texture according to the actual stage of each project. As part of the interface, we developed an ability to represent delays in the system, which are a major issue in multi-participant urban redevelopment projects.

The results of these maps can be seen in Figure 4. Figure 4(a) is an image from the simulated presentation of the statuses of all the projects during a given period. The user can scroll forward and backward during a defined period, or beginning at a specific point in time, to see the spatial development of the different projects. In lots with multiple construction formats, the state of buildings before and after is presented. Figure 4(b) displays the projects by typology, on a map and in 3D.

Figure 4.

Model of the urban redevelopment plans in the research area according to time-space patterns (using Esri ArcGIS Online).

The platform with which we worked did not allow us to link the time axis to the 3D model, although this linkage proved especially important for creating an intuitive and game-like interface that invites users (such as residents or their representatives on the planning and building committees) to investigate the environment along the axis of time. We therefore developed a separate 3D model (in a mixed ArcGIS/QGIS environment) capable of retrieving data from GIS and of following the progress of new building along the axis of time. Figure 5, for example, reflects the projected future situation of the research area in August 2025. Transparent blue represents planned construction, and red fill within it indicates the progress of building construction. The higher the red fill, the closer the building process is to completion.

Figure 5.

3D model of the duration of construction.

Space-time analysis

In the sixth stage, we opted to undertake a visual analysis of the patterns of urban redevelopment based on heat maps using a QGIS platform. Heat maps move along a time axis and show that the implementation of redevelopment projects in the study are likely to be non-linear. Some years witness concentrations of planned projects, whereas other years reflect other areas of adjacent construction sites that operate simultaneously. We discovered that the urban redevelopment resulting from self-organization creates clusters, or new soft boundaries within the city (Savini, 2016).

In our simulation, the redevelopment patterns of the various projects of self-organization are consolidated into territorial sites of planning or construction time, to which we refer as “territories of time.” Figure 6 reveals the hidden territories of time as planning patches (in blue) or building patches (in red) that appear on the map at specific times. When the stage's duration is longer, the color in the center of the patch is more saturated, as is its radius of impact.

Figure 6.

Heat maps representing space-time patterns using concentrations of planning and construction over the years (using QGIS).

We recommend devoting planning attention to these territories, which are trapped in time, and treating them as delicate tissue in need of special treatment, by formulating redevelopment policy suited to specific parts of the city. Based on the model's prediction ability, it will be possible, for example, to regulate a maximum redevelopment capacity for each cluster at a given time and to determine an index for the possible density of redevelopment projects. This will enable the rectification of heavy loads and imbalances (Brandão et al., 2018). If, as noted, “urban planning is increasingly coming to focus on nurturing economic vitality through processes of self-organization,” it has the responsibility to elicit public investment and to support areas of high-intensity redevelopment. For this purpose, funds may be allocated ahead of time for the enrichment of public institutions, parks, and required infrastructure, whether temporarily for the construction period, or permanently.

Discussion and conclusions

As formulating a space-time multidimensional (2D, 3D, and 4D) toolbox could lead to a breakthrough in urban planning practice abounding with site-specific, self-organized plans, we proposed this project and demonstrated it in a city that is currently undergo accelerated redevelopment. The proposed toolbox, relying mainly on ArcGIS Online and QGIS platforms, can contribute to advanced city information modelling by integrating self-organization dynamics into the urban systems. In this way, it can help local planning departments manage and monitor urban dynamics and enable developers, stakeholders, and residents to situate their self-organization vis-à-vis other organizational efforts in the city.

To incorporate planning into the dynamics of urban complexity, we sought to simulate the postmodern perception of time as an incalculable number of syntheses and interactions of unique chaotic events. For this purpose, we selected an approach that displays changes over time using vector layers, based on the principle of entity-based representation of spatial-temporal plans. We integrated the 3D model into the dynamic temporal map to yield a 4D model. Heat maps were used as a tool to analyze the effect of the phenomenon. We believe that this project stands to advance a more interactive, self-organizing, and interconnected planning paradigm (Potts, 2020). By proposing a set of tools to move toward the shift inherent in self-organization, even in its enactment as multi-spot zoning, we join the effort to lead planning practice “from an approach based on the reduction of complexities and the stabilization of dynamics to an approach based on the embracing of complexities and processes of ‘becoming’” (Boonstra and Boelens, 2011: 118).

The framework suggested in this article advances the applied usage of this conception for the sake of urban management led by the planning system. This framework is meaningful in three ways. First, it reflects the need to further research formats of time as part of the spatial planning array. Researching space-time patterns enables us to trace different tracks of redevelopment and their intensity and to reveal hidden territories, to which attention must be paid in spatial planning. Second, the time configurations of planning and building hold marked implications for urban management in realms such as infrastructure, social welfare, education, and economy. For example, the platform can promote future simulation regarding social issues such as migration and the displacement of long-time residents due to urban redevelopment. The study's limitation lies in the fact that it has not yet achieved this goal, although it does propose a basic methodology and outline a trajectory for doing so. A planning system will be able to navigate the coordination among these realms if it can create a unified dynamic system based on data on multiple axes. Third, expanding the conceptualization of the involvement of residents and stakeholders in urban planning could enrich the quality and patterns of life and behavior during housing regeneration. For example, further development of the simulation could help better manage the health of the expected future population of the site of ongoing construction. This pertains both to the real-time provision of accessible information regarding the implications of planning and to the everyday conduct of all actors in the city – in space, where building is in constant motion.

Future studies based on this model could assess new buildings' implications for their surroundings during their planning or construction. Future models could incorporate measurement of different aspects of redeveloped areas – such as noise, infrastructure work, the quality of nearby commercial roads, a plan to address educational needs, and more – and pave the way for interdisciplinary studies. For example, they can make it possible to assess and to run scenarios of municipal economy based on redevelopment executed in income-yielding areas, income from fees collected at different stages of the process, and projected city expenditures. The simulation could also help contend with problematic aspects, such as assessing future apartment prices stemming from housing shortages or, alternatively, from flooded housing markets in specific areas.

Although the proposed prototype model operates as a closed system based on real-life data regarding the planning system, its true potential lies in its ability to become an open system into which projects, alternatives, and changes can be read to facilitate ongoing discussion among planners, stakeholders, and residents. It is our hope that the system's implementation will develop into dynamic coordination among the different projects in a manner that makes site-specific local revisions to comprehensive zoning plans more democratic.

In conclusion, in this study, we offered a practical framework for the self-organized urban redevelopment that is characteristic of the post-zoning era. The criticism of planning fragmentation, we argued, has stemmed from the fact that the concept of a combined time-space is still not commonly used, and that neither are technological tools for sophisticated management and monitoring. Raising awareness of the social aspects of site-specific planning requires us to consider incorporating an array of terms and criteria regarding the organization of planning and building duration as part of the conception of planning as a complex and flexible system. It will therefore be necessary to continue researching and developing the toolbox for contending with the profound challenges of site-specific planning.

Footnotes

Acknowledgments

Daphna Levine is grateful to the Azrieli Foundation for awarding her an Azrieli Fellowship. The authors thank the anonymous reviewers for their valuable comments and criticism on earlier versions of this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Daphna Levine

Daphna Levine e is a PhD candidate in the Faculty of Architecture and Town Planning at the Technion – Israel Institute of Technology. Daphna is engaged in the search of routes to integrate social issues into urban planning, by creating models of concrete areas for representing abstract social phenomena. Specifically, her research examines urban redevelopment as an opportunity for social mobility of the lower-middle-class. By combining cutting-edge technology (e.g. Spatial Microsimulation) and qualitative research, Daphna strives to encode and assess the demographic dynamics and social impacts of urban redevelopment processes.

Shai Sussman is a Civil Engineer in Geoinformation and enrolled master's in Urban Planning at Technion – Israel Institute of Technology. As part of his thesis, currently, he is involved in joint research between Cornell-Tech and the Technion, where he is developing a spatial-microsimulation platform in the domain of urban planning. The simulation's role is to simplify complexity questions and asset the social impact within time and space regarding urban re-developments and economic changes. Shai has been involved in various types of developments that involved mapping the environment, both in commercial and open-source environments

Meirav Aharon-Gutman is a faculty member at the department of Architecture and Town Planning Technion, the academic Head of the Social Hub, Technion and Acting member at the National Council for Planning and Building, Israel. Meirav is Fulbright (twice) and Marie Curie award scholar. Her main research project is to translate social issues (mainly spatial inequality) to form, line and color in order to literally put them on the policy makers table: by doing so she shape new medium for Social Impact Assessment, she develop Data driven Policy simulation, and built Smart social theatre.

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