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The Java Model — a time-weighted network analysis of the desakota
19 Oct 2021
| By
Neville Neville Mars
Fabien Fabien Pfaender
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Discipline
Urban planning (urban design) cancel
Keywords
Rural-urban Integration
Network Analysis
Java
Gis
Indonesia
Desakota
Track:
“Urban-rural Integration” And “Areas In-between”
Abstract
Peri-urban landscapes present a growing challenge for urban planners. Vast territories that comprise high population densities, but few clear centralities, erode the epistemological integrity of popular planning models. Meanwhile, as in situ urban-industrial development and top-down planned infrastructures transform these intricate landscapes, both the conceptual and practical challenges augment. Here, geospatial data can provide valuable insights. Network analyses can visualise the transformations within the peri-urban morphology. However, common ‘unweighted’ network graphs don’t reflect operational realities on the ground and thus fail to inform planning strategies. This paper explores combining distance and travel speeds to develop a ‘time-weighted' network model of the desakotas of Central Java. A one-hour travel boundary is introduced to demarcate the study area. Inadvertently, this reveals a regional loop that follows the expanding highway system, which suggests a limited efficacy of tollroad developments. In response, this model is applied to evaluate a typical planning scenario: to build, or not build a new tollroad. The paper concludes that, the complexity of the impact on local communities, landscapes, and the regional ‘accessibility profile’ demands multi-scalar, multifaceted impact analyses to apprise strategic planning.

  *Content automatically extracted from the publication file

Type of the Paper: Peer-reviewed Conference Paper / Full Paper

Track title: 1. “Urban-rural Integration” and “Areas In-Between”

The Java Model — a time-weighted network analysis of the desakota

Neville Mars 1, Fabien Pfaender 2

1 Dynamic City Foundation / Mars Architects, Shanghai, China; nevillemars@me.com

2 UTSEUS, Shanghai University / Costech EA2223 Université de Technologie de Compiègne; fabien.pfaender@utc.fr; 0000-0001-6338-9058

Names of the track editors:

Alexander Wandl

Rodrigo Cardoso
Names of the reviewers:


Journal:
The Evolving Scholar 


DOI:
10.24404/6155dbc224a29800097051ff

Submitted: 09 October 2021

Accepted:

Published:

Citation: Mars, N. & Pfaender, F. (2021). The Java Model — a time-weighted network analysis of the desakota. The Evolving Scholar | IFoU 14th Edition.

This work is licensed under a Creative Commons Attribution CC BY (CC BY) license. 

©2021 [Mars, N. & Pfaender, F.] published by TU Delft OPEN on behalf of the authors. 

Abstract: Peri-urban landscapes present a growing challenge for urban planners. Vast territories that comprise high population densities, but few clear centralities, erode the epistemological integrity of popular planning models. Meanwhile, as in situ urban-industrial development and top-down planned infrastructures transform these intricate landscapes, both the conceptual and practical challenges augment. Here, geospatial data can provide valuable insights. Network analyses can visualise the transformations within the peri-urban morphology. However, common ‘unweighted’ network graphs don’t reflect operational realities on the ground and thus fail to inform planning strategies. This paper explores combining distance and travel speeds to develop a ‘time-weighted' network model of the desakotas of Central Java. A one-hour travel boundary is introduced to demarcate the study area. Inadvertently, this reveals a regional loop that follows the expanding highway system, which suggests a limited efficacy of tollroad developments. In response, this model is applied to evaluate a typical planning scenario: to build, or not build a new tollroad. The paper concludes that, the complexity of the impact on local communities, landscapes, and the regional ‘accessibility profile’ demands multi-scalar, multifaceted impact analyses to apprise strategic planning.

Keywords: Rural-urban integration, network analysis, Java, GIS, desakota, Indonesia

1. Introduction — data and the desakota

Gliding over the digital map, Java’s density spikes merge to form a thick cloud [Fig. 1]. This abstraction refutes any hard distinctions between city and countryside. Observing the desakota1 , Java’s version of a peri-urban landscape, contemporary urbanity reveals itself as simultaneously dispersed and increasingly tightly networked together. Loosely aggregated into a fragmented, yet paradoxically fused into a single cityscape, Java’s data topography reaffirms the ontological end of discrete urban nodes on a Cartesian plane, or what used to be simply known as ‘the city’.

Figure 1. Population density map of Java Island. Census 2020. Circle insert: typical desakota striation, north of YIA airport, Central Java.

The concept of the city as a 'seamless whole,’ has expired (DeLanda 2006: 10). As unsurprising as the conceptual death of the city would be to urban theorists, going right back to Deleuze, Alexander and Bateson in the seventies, in recent decades this notion has become dramatically more relevant, notably across Asia’s peri-urban landscapes.

In few places is Deleuze’s reconceptualisation of the city as an ‘assemblage’ more acutely pertinent than in the fringes of ‘Metro Java’, i.e. the urban cores within the island’s rural-urban continuum. Where metropolitan intensities merge with the rhizomatous settlements of the hinterland, the last vestiges of a binary ideology — of urban versus non-urban — are under attack. Rather than urban densities seeping into the spatial void of the city’s imagined counterpart, the countryside, in populated agrarian societies, built-up and densely populated areas invariably merge with the multiplicities of productive landscapes.

Java’s hinterland was never an urban void, not even a casual assemblage. The configurations of the desakota landscape have formed over centuries, to systematically follow the variations within the natural geography. A lattice of rural communities continuously interweaves with the topographies of fertile land, fresh water and forest. Resultant settlement patterns occurred in direct correlation to resource availability. The smallest unit of resource independence can be defined as a landscape ‘module’2. Occurring in a range of different forms, they are the building blocks of Java’s rural habitation. Innumerable modules link together to generate the desakota. In Deleuzian terms, this is a prime example of a “smooth space”3, i.e. flexible, generative, and decentralised. Yet, ironically, the resulting morphology that the desakota land use module delivers, visually resembles its counterpart: the “striated space”, characterised by central control, intent, and hierarchy. (Deleuze & Guattari, 1980).

The strands of land use types within each module link up to form a striated system of extended functional bands, which, we will argue, are critical to the success of the desakota. Preserving and restoring these linkages is a core objective of the larger “Metro Java 2045”4 planning project that this study is part of. The underlying premise is that the desakota’s uninterrupted land use striation can be equated with land use efficiency, sustainability, and even resilience, as defined in Indonesia’s Climate Roadmap (ICCSR).

2. Time-based network analysis

Without significant hierarchies or centralities within its spatial networks, the desakota defies conventional planning logic built around the dichotomy of centre and periphery. The planning discipline hinges on this withering spatial construct, which in turn underpins other fundamental, yet increasingly contrived, planning polarities: urban/rural, planned/generated, top-down/bottom-up, formal/informal, productive/stagnant, access/isolation, development/preservation, and notably, global/local.

As the desakota absorbs all the generic components of a global, neoliberal urbanism, its ‘smooth spaces’ give way to disruptions and emergent hierarchies. Yet, while planning conventions collapse within the sphere of the desakota’s spatial transformations, maturing data technology brings its geospatial analysis within reach.

Spatial datasets allow us to map and analyse Java’s topological landscape within a rural-urban continuum5. As the desakota’s spatial structure shifts from a predominantly horizontal ‘field condition’ to incorporate growing spatial hierarchies, its performance remains tied to the topology of mobility corridors and their ensuing land use patterns. Geospatial data allows its network structures to be modelled across scales (i.e. foot and bike paths (1.5m), single lane, and ‘one and a half lane’ roads (3m to 4.5m), trunk roads, and (planned) tollways.

According to Waldo Tobler’s First Law of Geography "everything is related to everything else, but near things are more related than distant things”. Within a technological and socioeconomic context of an urban assemblage, proximity is as much defined by time as it is by distance. Spacetime is as much a reality for urbanists, as it is for theoretical physicists. To this end, the network analysis presented in this paper moves beyond common ‘unweighted' graphs to include parameters of distance and travel speed, from which, in turn, a system of travel times can be deduced.

While in recent years time-based planning strategies have become commonplace, in daily practice few models seem to be backed by empirical evidence. Concepts such as the 5/15/30/60-minute city models hold promise to bring critical amenities within reach of residential communities. The objective to concentrate urban functions, people and mobility echoes other sustainable planning tropes, such as Transit Oriented Development (TOD), and the ‘compact city' writ large. A discipline-wide accepted definition for the compact city model is still lacking, but as the theoretical cornerstone of sustainable planning, its premise is that more condensed populations provide support for high-end public transit, services and amenities, which in turn shorten commute times, frequency and trip length, and thus lower energy demand. Although its scientific application remains challenging and the model is fraught with epistemological contradictions, as a collective imaginary the compact city coaxes efforts across a spectrum of planning specialisms towards shared goals of mixed and intensified land use.

The 15-minute city model is reminiscent of 90s “village-in-city” schemes that aimed to disperse urban programme throughout the city in order to limit suburb-to-centre mobility needs. As renowned urban planner Alain Bertaud has abundantly argued, this would subvert the very “reason d’être”6 of the city as a place for a large mobile labor force to congregate, thus improving its citizens‘ ability to seek employment and housing opportunities. Edward Glaeser, shares a similar sentiment for the 15-minute city, calling it “an enclave — a ghetto – a subdivision”7, which, he argues, undercuts the city as an archipelago of neighbourhoods, which should foster links between different social territories, as much as within them. But good criticism isn’t obvious within the context of Java’s desakota. Planned centralities can offer vital amenities and economic opportunities, whilst invariably increasing pressure on fragile ecologies and intricate road systems.

Accessibility and transit solutions are site specific. They have to be observed over the long-term and within their local context, through a lens of culture, scale, form, morphology, density, climate, etc. Yet, surprisingly, many global institutions and thought leaders (ADB, WB, Brookings Institution, Joan Close8, Parag Khanna9) still tend to uncritically equate economic development with road construction. This foregoes the precarious progress/preservation balance that emerging, tropical economies face. The traditional desakota communities were highly autonomous and localised. Their growing dependence on urban economies and tourism, and adoption of urban lifestyles, draws in urban components of a supra-regional reach. Expansion of infrastructural networks both supports and fuels this trend. In turn, this augments in situ urbanisation and socio-ecological fragmentation10.

Roads intersect as much as they connect. Crudely planned infra-corridors vitiate the desakota’s unique landscape striation. The desakota’s relative isolation is both its weakness and its strength. Yet, current development models reveal a bias for highway expansion. Densely populated productive landscapes, typically in monsoon regions, are the world’s dominant form of habitation. Intersecting these rice baskets with highways often presents an obvious choice for policymakers and market actors alike. The boost it gives local economies can be quick and construction relatively easy. But at a time when grassroots activism has forced inner-city highways to be repurposed, demolished, and their construction blocked, in France, Korea and India, respectively, investigating viable alternatives for the desakota and other peri-urban landscapes, reflects a global urgency.

3. The one-hour city: framing a network analysis of Central Java

3.1. Green edge

Data driven analyses have begun to unveil the innate complexities of peri-urban landscapes. However, operating within the vast expanse of Central Java’s hinterland presents a technical challenge: how to demarcate the boundaries of its geospatial analysis? This question echoes the predicaments planners face when setting urban growth boundaries within the context of scattered and evolving cityscapes. From a performance and user perspective, Bertaud argues, one hour is a cut-off point for a “reasonable” commute. However, applied to large urban regions, with many inaccessible pockets, this presents a muddled accessibility profile. Previously, we have contended that precisely within vast, decentralised urban networks, a ‘one-hour city’ concept can offer clarity as to where — and where not — to develop (Mars, Hornsby 2008). We’ve applied a similar time-based constraint to delineate our analysis of Central Java’s rural road system.

3.2. Polar opposite

Within the 130x130 km Central Java study area, the green edge neatly encapsulates the polar structure of the regional transit corridors. Steadily, these national roads are upgraded to form a loop of tollways, with the volcano of Merapi Mountain at its centre. This simple hierarchy provides an ideal context to observe the transitions of the region’s accessibility profile. We set out to model and describe impact on travel times, which in turn should inform planning decisions on how to guide ensuing urban patterns. However, this simplicity proved misleading, as it obscures the study area’s complex internal organisation. We estimate this is about the largest area current data analysis can adequately sustain before upsetting its viability threshold.

Figure 2. Study area of 130x130km (red), and the one-hour travel envelope (black).

3.3. Arborescence

Island-wide tollway construction is a national planning project. Their accelerated emergence, driven by an unofficial objective to match Western level road densities by 2045, could be interpreted as Indonesia merely catching up with the world — infrastructurally and, consequently, economically. For the study area this is a total road surface of 873 km2 and a relatively high land coverage of 5 %. If we consider road level distributions, the surfaces are unequal, with residential and tertiary roads accounting for 806 km2 while secondary and primary roads account for only 35 km2 and with 13 km2 for trunk and highways. However, we aim to illustrate that in order to make such comparisons, the morphology of the network is a paramount consideration. Java’s developmental pathway is unlike western European countries, and certainly unlike today’s juggernaut of highway construction, China. In general terms, the former gradually strengthened their historical trade routes to connect Europe’s compact urban cores with bundled transit corridors, while the latter is in the process of projecting a universal road grid onto its greenfields. They represent two extremes of highway development: the arborescent structure of a gradually maturing transit plexus, versus the uniform lattice of a built-from-scratch highway grid system.

It seems unreasonable, however, to categorise Java’s road-network-in-transition as either one. The filigrain system of footpaths have served the desakota for decennia. Despite its extreme population densities, the homogeneous dispersion and localisation of activities allowed the network to function without forging much hierarchy. The desakota’s landscape modules tessellate across the plane in an organic tiling, incorporating connections where needed. In this system, a tollroad is not an upgrade or an emergent system, but a wholly novel entity, comprised of its own scale and speed, which will induce its own socioeconomic, spatial, ecological, and network dynamics.

3.4. Describing the one-hour corridor

The one-hour city model simulates a space of 30-minute access on either side of the highway. The resultant envelope is a single, undifferentiated entity of a one-hour commute from edge to nearest edge, perpendicular to the highways at its centre. This envelope can be defined using a precise geospatial methodology. The raw data is derived from OpenStreetMap (OSM). Cropped to our 130x130km frame, this dataset of 2gb, provides the basis of a 4-steps adjustment process that ultimately yields the one-hour envelope.

The first transformation distills the roads from the OSM map dataset, which include a variety of other geographic qualifiers not applicable to this analysis. However, currently only a few distillation tools can handle the large size of this dataset. We’ve applied the parser library policosm11 in Python language, developed specifically for such an operation. It does so by scanning the map and selecting connections with the tag ‘highway’. This tag can be associated with a multitude of values, so we have introduced a system to reduce this variety to seven grades, as defined in the following reference table.

Value of the highway tag level
services, bus, busway, bus_guideway, access, bus_stop, via_ferrata, access_ramp, emergency_access_point, emergency_bay, service, footway, traffic_island, virtual, cyleway, cycleway, byway, path, track, pedestrian, steps, platform, bridleway, rest_area, escape, footway 2
residential, yes, unclassified, crossing, unknown, bridge, lane, ford, psv, living_street, alley 3
tertiary, tertiary_link, turning_circle, road, roundabout, ice_road 4
secondary, secondary_link 5
primary, primary_link 6
trunk, trunk_link 7
motorway, motorway_link, ramp 8

Table 1. Overview of road levels 2 to 8 and the descriptions of the types they contain.

From here, the algorithm builds the network out, translating each point along the way as two vertices joined by an edge. At this stage, the roads are represented in a uniform graph. The second transformation is to refine the road network. However, the network contains too many vertices and edges for standard network algorithms to perform operations, such as a routing or centralities assessment, in a finite time. Therefore, simplifying the network while retaining its spatial information is critical in this process.

The third transformation is to crop the 130x130km area to create the corridor, or spatial envelope. This operation starts with establishing a series of anchor points throughout the area running from north to south, from Semarang to Yogyakarta airport, in a loop encircling the volcano. The anchor points are geographic tethers; once attached, the set of 17 anchors points are joined using a routing shortest path search weighed in time. The time factor itself is a specific feature added to every edge corresponding to its geographical length (in metres, projection UTM zone 49S), multiplied by the speed observed on such edges, effectively ranging from 10KPH to 38KPH (Munawar 2011). The routing algorithm then generates a path that minimises the commute between each pair of successive anchor points. This delivers the basic network skeleton. From this skeleton, the one-hour corridor is simulated. The routing algorithm is employed once again, now weighted for time. For the corridor, the idea is to create a sub-network, or 'isochrone tree’, from each of the 812 vertices of the backbone expending a maximal 30min travel time. These 812 branches create the time threshold, which are merged within a global network. This produces the final network dataset. The fourth transformation attributes an envelope to this one-hour corridor. It does so by creating a concave hull around each of the 812 tree-shaped networks and performing a 'unary union’ at each of them.

Figure 3. One-hour envelope projected onto settlement patterns of Central Java

4. Exploratory interpretation of the network data

This arduous process allows us to observe a more complete picture of the dense, singular network serving the desakota. Comparisons between travel time and travel distance, either using or avoiding the highway corridor, can now be made for the commute between any two points within the one-hour city loop.

The particularities embedded within the desakota, as described in the introduction, begin to reveal themselves — specifically its decentralised and dispersed structure. The visualisation itself validates our initial assumption that data analysis can offer a means to collaborate on conceptual problems, such as time-based planning models. Moreover, the network offers a meaningful tool for planning practitioners to formulate and assess concrete spatial strategies.

Figure 4. Closeness centrality ranging from 0 light blue to 1 dark purple.

As a means to stress-test the cross-disciplinary dialogue, we have performed two simple experiments using two centrality measures commonly used in graph theory on the 367,000 edges within this network (Porta 2010). Both centralities are based on ‘shortest path’ computation, which in this model is computationally intensive for the graph-tool library (Peixoto 2014). The shortest path minimises the sum of the weight (in our case, time-distance) between two vertices, calculated for the centrality measure for every pair on the graph.

4.1. Closeness centrality

Closeness centrality reflects how close a vertex is to all the other vertices on the graph, i.e. the sum of the distance weight of all the shortest paths between one vertex and all the other vertices on the graph. The rendered outcome is presented in figure 4.

The higher (darker) values of the closeness centralities appear on the south east side of the network, with the exception of the backbone, i.e. the highways and tollways. This centrality is sensitive to how many vertices are in close vicinity with one another; in our one-hour model, this is highlighted in the south-east region. All vertices in this area are close to each other, forming an almost ‘optimal’ lattice. In norther part of Semarang the network is also dense, so closeness centrality is expected to be higher. However, this is not the case as transit systems within the city are more hierarchical, thus closeness reveals that only a few corridors connect many destinations. Conversely, a lattice-like network with few hierarchies brings all vertices close to each other in a non-hierarchical manner. This reveals that multiple destinations can be reached via multiple connectors without a significant drop in travel time. This render suggests that the villages can exchange across this vast area time efficiently, without relying on one specific set of connections where traffic would subsequently concentrate.

4.2. Betweenness centrality

To build on this idea, we have chosen to implement a ‘betweenness centrality’ algorithm, weighted again with distance as a factor of travel time between vertices A and B. This centrality is different from proximity, as instead of measuring how close each vertex is to all the others, it measures how many of the shortest paths from all vertices to all other vertices pass through each edge. The edges with a high betweenness score are more likely to get traffic. In a hierarchical city, the betweenness is usually found at cities’ main arteries, such as trunk or primary roads, or bridges. The outcome of betweenness centrality is visualised below.

The betweenness in the figure has been filtered from the highest values. The reason for this is that the edges from the backbone has been obtained using the shortest path method in the first place, so their betweenness score eclipse the complexity of the underlying lower betweenness values. To remove the higher values we simply remove one per thousand of the highest values. The betweenness values in the network show two different situations: the urban city situation where the edges are very dense, and the desakota or village situation, where edges are less dense.

In cities, this centrality is not very helpful as cities are hierarchical structures and removing the main travel routes result in tiny sparks of high betweenness on secondary and tertiary roads that hold no real value. The desakota structure, on the other hand, shows longer paths of high betweenness, spread among its vicinity, indicating a village-to-village favoured path with fewer low values (depicted as yellow and green in Figure 5). The combination of fewer low values and a high closeness indicates an optimisation of the land, where each connection is valuable, serving as a support for traffic with fewer additional options. The optimal network centralities observed here are a good indication of the values of desakota in this specific urban-rural structure that hold the key to a sustainable densification.

5. Planning scenario and dialogue

5.1. Fait accompli

A reflexive process is elementary to urban planning. Feedback should be garnered at all stages of a project, and from all stakeholders. Though this principle isn’t really disputed, it is not often implemented. Precisely in regions where planning conditions are most challenging, and development projects would benefit most from critical evaluation, time and resources tend to fall short. In large countries with complex power structure s, such as Indonesia, implementing a long-term supra-regional planning vision is a particularly painstaking and largely opaque process. Decisions that have been made will, therefore, not soon be overturned, or indeed critically discussed in the public domain. Tollway expansions in Central Java that have received a green light, are in effect a fait accompli.

Figure 5. Betweenness centrality of the network edges are thresholded for the highest values as to let the lower structure appear.

Nevertheless, with thousands of kilometres of new infrastructure on the agenda nationwide for 2045, the transformations of Central Java should serve as a valuable case study. As its highways are rolled out to create better access for tourism in culturally significant locations, such as to the Borobudur, there seems to be a broad consensus among local experts12 that this must be done sensibly. What this means, however, or how to achieve this — i.e. building elevated highways, fewer on and off ramps, tunnelling — remains to be seen. What is clear, as many have argued before us13, is that the desakota is foremost a fragile heritage landscape. The success of the region will be rooted in the success of its rural communities, which in turn remains reliant on the integrity of the village-landscape modules linking up to form the desakota. This underscores that spatial visions for the region cannot be orchestrated through central planning alone. Decisions on highway development should be based on an holistic assessment, informed by objective data points. Herein lies a challenge. Additionally, the luxury of a long-term perspective, which anticipates the advent of new technologies, allows us to believe alternative development models are feasible. Such models localise resource flows (i.e. smart grids), and will allow remote communities to virtually tap into global information flows (i.e. e-learning) and revenue streams (i.e. e-commerce). These are potentially new forms of regional development, which wouldn’t put further strain on existing road networks. What they do not offer is immediate solace for regions struggling simultaneously with congestion and pockets of inaccessibility. This is a conflict of ideologies representing progress and preservation, enhanced by a gap between theory and planning practice.

6. Conclusions

In contrast to the radial accessibility profiles that form around public transit nodes, the one-hour envelope we have modelled, portrays the desakotas of Central Java as a single ~250km long linear city. The operational direction of this loop, however, functions as much along the highways, as it does perpendicular to them. The tollways within the envelope represent progress and, as this simulation indicates, a new centrality. Yet, this is a centrality that is paradoxically void of urban programme. This contrast suggests that the emergent hybrid landscape of this region is a product of the collisions between two urban configurations: tree and tessellation — the respective products of two disparates of urbanism, local and global. The model indicates that most village-to-village connectivity doesn’t improve and can even deteriorate with the inclusion of a tollway at its centre. To determine for which villages precisely this happens, further research is being prepared.

Data Availability Statement

Global Land Analysis & Discovery (https://glad.umd.edu/dataset/global-2010-tree-cover-30-m) (Hansen et al., 2013)

OCHA Services (https://data.humdata.org/dataset/worldpop-population-counts-for-indonesia)

EEA (https://www.eea.europa.eu/data-and-maps/data/world-digital-elevation-model-etopo5)

Contributor statement

Initiator: Neville Mars

Data Curation: Fabien Pfaender

Formal Analysis: Fabien Pfaender

Investigation: Fabien Pfaender, Yeeun Boo, Harshitha Mruthyunjaya, Neville Mars

Methodology: Fabien Pfaender, Neville Mars

Visualization: Fabien Pfaender

Writing: Neville Mars, Fabien Pfaender

Editing: Eva-Mae Brazil

Acknowledgments

This study is part of a 3 year planning project by: MARS Architects / Dynamic City Foundation + Krill o.r.c.a., with UNDIP, Gajah Mada University, with support from Yogyakarta Heritage Society and the Ministry of Transportation of Indonesia. With generous support by the EFL Stichting and the Dutch Creative Industries Fund. For periodical updates: www.MetroJava2045.org.

References

Deleuze, G., & Guattari, F. (1980). A Thousand Plateaus: Capitalism and Schizophrenia. Paris: Les Éditions de Minuit.

Munawar, (2011) A. Speed and Capacity for Urban Roads, Indonesian Experience. Procedia - Soc Behav Sci 16, 382–387.

Porta, S., Latora, (2010) V. & Strano, E. Network Science, Complexity in Nature and Technology. 107–129 doi:10.1007/978-1-84996-396-1_6.

Wood, J. W.(2020) The Biodemography of Subsistence Farming, Population, Food and Family. Part of Cambridge Studies in Biological and Evolutionary Anthropology., Pennsylvania State University, June

Cairns, S. (2002). Troubling real estate: Reflecting on urban form in Southeast Asia. In 910888161 716897344 T. Bunnell, 910888162 716897344 L. B. Drummond, & 910888163 716897344 K. Ho (Authors), Critical reflections on cities in Southeast Asia (p. 118). Singapore: Times Academic in association with Brill Academic.

Ahmadzai, F. , Rao, K.M.L., Ulfat, S. (2019) Assessment and modelling of urban road networks using Integrated Graph of Natural Road Network (a GIS-based approach), Journal of Urban Management, Volume 8, Issue 1, Pages 109-125, https://doi.org/10.1016/j.jum.2018.11.001.

Rui, Y., & Ban, Y. (2011). Urban growth modeling with road network expansion and land use development. In Advances in Cartography and GIScience. Volume 2 (pp. 399-412). Springer, Berlin, Heidelberg.

Tunas, D., (2019). flows are defined by an interdependence of between economic principles of exchange and their enabling infrastructures in Archipelago Cities: Planning beyond urban boundaries, Social urban spatial correlation (Harvey 1989; Lefebvre 1991)

Peixoto, T. P., (2014) “The graph-tool python library”, figshare. DOI: 10.6084/m9.figshare.1164194 [sci-hub, @tor]


  1. Desakota is a peri-urban landscape typology of Java Island. The term, coined by Terrence McGee, is an agglutination of the words ‘desa’ (village) and ‘kota’ (city) used to describe the growth and features of areas of mixed urban and agricultural activities that characterize the previously rural hinterlands of many of the rapidly expanding urban centers of the developing world in a new era of ‘planetary urbanization’.” McGee T.G. (2017) The Sustainability of Extended Urban Spaces in Asia in the Twenty-First Century: Policy and Research Challenges. [In: Yokohari M., Murakami A., Hara Y., Tsuchiya K. (eds) Sustainable Landscape Planning in Selected Urban Regions. Science for Sustainable Societies. Springer, Tokyo.]↩︎

  2. A second paper of Metro Java 2045 charts these land use modules. “A Cluster-based land use taxonomy for Central Java”. Boo Y., Mars, N.↩︎

  3. A Thousand Plateaus: Capitalism and Schizophrenia, p474, Gilles Deleuze, Félix Guattari, “Mille Plateaux”, Les Éditions de Minuit, 1980.↩︎

  4. www.MetroJava2045.org↩︎

  5. Vast territories notably in India, China and Indonesia that constitute a hybrid landscape typology characterised by (fragments of) urbanisation dominating vast rural settings. Here it spawns urban functions and micro centralities, without clear dependencies to nearby urban cores. Lacking contiguity, urban density and centrality, yet integrated with the urban economies that surround it, the rural//urban field defies notions of city. The Chinese Dream - a society under construction. Lexicon. N. Mars, A. Hornsby. 010 Publishers, Rotterdam 2008.↩︎

  6. How Markets Shape Cities. Bertaud, A. 2018 MIT Press.↩︎

  7. The 15-minute city is a dead end — cities must be places of opportunity for everyone. LSE conference: The 15-Minute City. May 28th, 2021. https://blogs.lse.ac.uk/covid19/2021/05/28/the-15-minute-city-is-a-dead-end-cities-must-be-places-of-opportunity-for-everyone/↩︎

  8. As Former Executive Director of the United Nations Human Settlements Programme Dr. Joan Clos argues at UNH III, Quito, 2016.↩︎

  9. Connectography - Mapping the future of global civilisation. Khanna, P. 2016 Penguin Random House.↩︎

  10. Meijer JR, Huijbregts MAJ, Schotten KCGJ, and Schipper AM. 2018. Global patterns of current and future road infrastructure. Environmental Research Letters 13: 064006. http://www.globio.info/download-grip-dataset↩︎

  11. https://zenodo.org/record/1478235↩︎

  12. http://metrojava2045.org/roadmap-borobudur/↩︎

  13. http://jogjaheritagesociety.org↩︎

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Mars, N. & Pfaender, F. (2021). The Java Model — a time-weighted network analysis of the desakota [preprint]. The Evolving Scholar | IFoU 14th Edition. https://doi.org/10.24404/6155dbc224a29800097051ff