superblockify: A Python Package for Automated Generation, Visualization, and Analysis of Potential Superblocks in Cities
Carlson Moses Büth Anastassia Vybornova Michael Szell

Understanding IoT Domain Names: Analysis and Classification Using Machine Learning
This work has been supported by ANR and BMBF within the PIVOT project (https://pivot-project.info/)

    Ibrahim Ayoub
Afnic – Université Paris-Saclay

Yvelines, France

ibrahim.ayoub@afnic.fr
Martine S. Lenders
Technische Universität Dresden

Dresden, Germany

martine.lenders@tu-dresden.de
Benoît Ampeau
Afnic

Yvelines, France

benoit.ampeau@afnic.fr
Sandoche Balakrichenan
Afnic

Yvelines, France

sandoche.balakrichenan@afnic.fr



Kinda Khawam
Université de Versailles St-Quentin

Versailles, France

kinda.khawam@uvsq.fr
Thomas C. Schmidt
HAW Hamburg

Hamburg, Germany

t.schmidt@haw-hamburg.de
Matthias Wählisch
Technische Universität Dresden – Barkhausen Institut

Dresden, Germany

m.waehlisch@tu-dresden.de

    April 29, 2024
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summary


§ SUMMARY


 is a Python package for partitioning an urban
street network into Superblock-like neighborhoods and for visualizing
and analyzing the partition results. A Superblock is a set of adjacent
urban blocks where vehicular through traffic is prevented or pacified,
giving priority to people walking and cycling
(ref-nieuwenhuijsen_superblock_2024Nieuwenhuijsen
et al., 2024). The Superblock blueprints and descriptive statistics
generated by  can be used by urban planners as a
first step in a data-driven planning pipeline, or by urban data
scientists as an efficient computational method to evaluate Superblock
partitions. The software is licensed under AGPLv3 and is available at
<https://superblockify.city>.

statement-of-need


§ STATEMENT OF NEED


The Superblock model is an urban planning intervention with massive
public health benefits that creates more liveable and sustainable cities
(ref-laverty2021lowLaverty et al., 2021;
ref-mueller2020Mueller et al., 2020;
ref-world2022walkingWHO, 2022). Superblocks form
human-centric neighborhoods with reduced vehicular traffic. They are
safer, quieter, and more environmentally friendly
(ref-agenciadecologiaurbanadebarcelona2021Agència
d'Ecologia Urbana de Barcelona et al., 2021;
ref-martin2021Martin, 2021;
ref-mueller2020Mueller et al., 2020) than
car-centric urban landscapes which fully expose citizens to car harm
(ref-miner2024carMiner et al., 2024). The
scientific study of Superblocks has expanded quickly in recent years,
summarized in a review by Nieuwenhuijsen et al.
(ref-nieuwenhuijsen_superblock_20242024). The
planning and implementation of Superblocks is an intricate process,
requiring extensive stakeholder involvement and careful consideration of
trade-offs (ref-nieuwenhuijsen2019Nieuwenhuijsen et
al., 2019; ref-stadtwien2021Stadt Wien, 2021;
ref-transportforlondon2020Transport for London,
2020). New computational tools and data sets, such as the
 Python library
(ref-boeing2017Boeing, 2017) and OpenStreetMap
(ref-openstreetmapcontributors2023OpenStreetMap
contributors, 2023), provide the opportunity to simplify this process
by allowing to easily analyze and visualize urban street networks
computationally. Recent quantitative studies on Superblocks have seized
this opportunity with different focuses, such as potential Superblock
detection via network flow on the abstract level
(ref-eggimann_potential_2022Eggimann, 2022a) or in
the local context of Vienna
(ref-frey2020potenzialeFrey et al., 2020);
development of interactive micro-level planning tools
(ref-abstreetCarlino et al., 2024;
ref-tuneourblockTuneOurBlock, 2024); green
space (ref-eggimann_expanding_2022Eggimann,
2022b), social factors (ref-yan_redefining_2023Yan
& Dennett, 2023), health benefit modeling
(ref-li_modeling_2023Li & Wilson, 2023), or an
algorithmic taxonomy of designs
(ref-feng_algorithmic_2022Feng & Peponis, 2022).
However, to our knowledge, none of these emerging research efforts have
led to an open, general-use, extendable software package for Superblock
delineation, visualization, and analysis.  fills
this gap.

The software offers benefits for at least two use cases. First, for
urban planning, it provides a quick way to generate Superblock
blueprints for a city, together with descriptive statistics informing
the planning process. These blueprints can serve as a vision or first
draft for potential future city development. In a planning pipeline,
 stands at the beginning, broadly delineating the
potential areas of study first. Then, exported Superblocks can feed into
an open geographic information system like QGIS
(ref-qgisQGIS Development Team, 2024) or into
tools like A/B Street (ref-abstreetCarlino et al.,
2024) or TuneOurBlock
(ref-tuneourblockTuneOurBlock, 2024) that
allow finetuned modifications or traffic simulations. This quick
feedback can reduce the time and resources required to manually plan
Superblocks, which in turn can accelerate sustainable urban development.
Second,  enables researchers to conduct
large-scale studies across multiple cities or regions, providing
valuable insights into the potential impacts of Superblocks at a broader
scale. In both cases,  can help to identify best
practices, algorithmic approaches, and strategies for Superblock
implementation.

The software has served in a preliminary analysis of potential
Superblocks in 180 cities worldwide
(ref-buth2023masterBüth, 2023) and will be used in
subsequent studies within the EU Horizon Project JUST STREETS
(<https://just-streets.eu>). With increased urbanization, impacts of
climate change, and focus on reducing car-dependence
(ref-mattioli_political_2020Mattioli et al., 2020;
ref-ritchie2018Ritchie & Roser, 2018;
ref-satterthwaite2009Satterthwaite, 2009), the
need for sustainable urban planning tools like 
will only increase
(ref-nieuwenhuijsen_superblock_2024Nieuwenhuijsen
et al., 2024).

features


§ FEATURES


 has three main features: Data access and
partitioning, Visualization, and Analysis.

data-access-and-partitioning


 §.§ Data access and
partitioning


 leverages OpenStreetMap data
(ref-openstreetmapcontributors2023OpenStreetMap
contributors, 2023) and population data GHS-POP R2023A
(ref-pesaresi2023Pesaresi & Politis, 2023). From
a user-given search query, e.g., a city name, 
retrieves the street network data of a city, the necessary GHS-POP
tile(s), and distributes the population data onto a tesselation of the
street network.

After the street network and optional metadata are loaded in, the
package partitions the street network into Superblocks. In its current
version 1.0.0,  comes with two partitioners:


enumi.

  * The residential approach uses the given residential street tag to
  decompose the street network into Superblocks.

  * The betweenness approach uses the streets with high betweenness
  centrality for the decomposition.


The resulting Superblocks can be exported in GeoPackage ()
format for further use.

visualization


 §.§ Visualization


After the partitioning, factors relevant for analysis and planning of
Superblocks can be calculated and visualized, e.g., area, population,
population density, or demand change by betweenness centrality. Example
Superblock configurations for two cities are shown in Fig.
<ref>.



analysis


 §.§ Analysis


For analysis, the package calculates various graph metrics of the street
network, such as global efficiency
(ref-latora2001Latora & Marchiori, 2001),
directness (ref-szell2022Szell et al., 2022),
betweenness centrality (ref-brandes2008Brandes,
2008), spatial clustering and anisotropy of high betweenness centrality
nodes (ref-kirkley2018Kirkley et al., 2018),
street orientation-order (ref-boeing2019Boeing,
2019b), or average circuity
(ref-boeing2019aBoeing, 2019a). These metrics are
calculated for the entire street network and for each Superblock
individually. To facilitate further analysis, all of these metrics are
included in the exportable GeoPackage file.

design


§ DESIGN


's design is object-oriented with a focus on
modularity and extensibility. An abstract partitioner base class is
provided to facilitate implementing new custom approaches for Superblock
generation. At the core of the package,  extends
Dijkstra's efficient distance calculation approach with Fibonacci heaps
on reduced graphs, ensuring optimal performance when iterating various
Superblock configurations while respecting the Superblock restriction of
no through traffic. This restriction is checked via just-in-time (JIT)
compilation through 
(ref-siu_kwan_lam_2023_8087361Lam et al., 2023) to
speed up the calculation of betweenness centrality on directed,
large-scale street networks. Central code dependencies are the
 (ref-boeing2017Boeing, 2017) and
 (ref-hagberg2008Hagberg et al.,
2008) packages for data acquisition, preprocessing, and network
analysis, and the 
(ref-joris_van_den_bossche_2023_8009629Bossche et
al., 2023) package for spatial analysis.

acknowledgements


§ ACKNOWLEDGEMENTS


Michael Szell acknowledges funding from the EU Horizon Project JUST
STREETS (Grant agreement ID: 101104240). All authors gratefully
acknowledge all open source libraries on which 
builds, and the open source data that this software makes use of: Global
Human Settlement Layer, and map data copyrighted by OpenStreetMap
contributors available from <https://www.openstreetmap.org>.

authors-contributions-with-credit


§ AUTHORS CONTRIBUTIONS WITH CREDIT




  * Carlson M. Büth: Conceptualization, Software, Investigation,
  Methodology, Writing – original draft, Validation

  * Anastassia Vybornova: Conceptualization, Supervision, Writing –
  review & editing, Validation

  * Michael Szell: Conceptualization, Project administration, Writing –
  review & editing, Validation, Funding acquisition


references


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