Merge remote-tracking branch 'refs/remotes/origin/submission'

# Conflicts:
#	docs/conf.py
#	superblockify/_version.py

docs/conf.py

@@ -24,7 +24,7 @@
 project = "superblockify"
 copyright = "2023-2024, superblockify developers"
 author = "superblockify developers"
-release = "1.0.0rc11"
+release = "1.0.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

paper/paper.md

@@ -38,14 +38,15 @@ bibliography: paper.bib
 
 # Summary
 
-`superblockify` is a Python package for partitioning an urban street network into
-Superblock-like neighborhoods and for visualizing and analyzing the partition results.
+`superblockify` is a Python package designed to assist in planning future Superblock
+implementations by partitioning an urban street network into Superblock-like neighborhoods
+and providing tools for visualizing and analyzing these 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 [@nieuwenhuijsen_superblock_2024]. The Superblock blueprints and descriptive
+cycling [@nieuwenhuijsen_superblock_2024]. The potential Superblock blueprints and descriptive
 statistics generated by `superblockify` 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
+a data-driven planning pipeline for future urban transformations, or by urban data scientists as an efficient
+computational method to evaluate potential Superblock partitions. The software is licensed under
 AGPLv3 and is available at \url{https://superblockify.city}.
 
 # Statement of need
@@ -92,7 +93,7 @@ This quick feedback can reduce the time and resources required to manually plan
 Superblocks, which in turn can accelerate sustainable urban development.
 Second, `superblockify` 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.
+Superblocks at a broader scale, e.g. travel time changes.
 In both cases, `superblockify` can help to identify best practices, algorithmic
 approaches, and strategies for Superblock implementation.
 
@@ -130,6 +131,9 @@ In its current version 1.0.0, `superblockify` comes with two partitioners:
 2. The betweenness approach uses the streets with high betweenness centrality for the
    decomposition.
 
+The choice between these two approaches depends on the data quality and the desired outcome.
+The residential approach is appropriate for using residential data, if available and accurate.
+The betweenness approach is an alternative based on traffic flow approximation.
 The resulting Superblocks can be exported in GeoPackage (`.gpkg`) format for further
 use.
 
@@ -141,17 +145,22 @@ by betweenness centrality.
 Example Superblock configurations for two cities are shown in Fig.
 \ref{fig:combined_graphs}.
 
-![Automated generation of Superblocks. Athens (top row) and Baltimore (bottom row) Superblocks generated using the residential partitioner (left column) and the betweenness partitioner (right column). The streets of each Superblock are colored, the rest of the streets are black. Colored nodes denote representative nodes within each Superblock for easier visual recognition. Map data from OpenStreetMap. \label{fig:combined_graphs}](combined_graphs.png)
+![Automated generation of Superblocks. Athens (top row) and Baltimore (bottom row) Superblocks generated using the residential partitioner (left column) and the betweenness partitioner (right column). Each Superblock is plotted in a different color, the rest of the streets are black. For easier visual recognition, each Superblock is also highlighted by a representative node of the same color. Map data from OpenStreetMap. \label{fig:combined_graphs}](combined_graphs.png)
 
 ## Analysis
 
-For analysis, the package calculates various graph metrics of the street network, such
-as global efficiency [@latora2001], directness [@szell2022], betweenness
-centrality [@brandes2008], spatial clustering and anisotropy of high betweenness
-centrality nodes [@kirkley2018], street orientation-order [@boeing2019], or average
-circuity [@boeing2019a].
+For analysis, the package calculates various graph metrics of the street network, including:
+
+- Global efficiency [@latora2001]: In the context of Superblocks, this measures how the overall ease of vehicular movement across the city might change after implementation.
+- Directness [@szell2022]: This indicates how Superblock implementation might affect the directness of routes, potentially increasing or decreasing detours.
+- Betweenness centrality [@brandes2008]: Identifies which streets might bear increased traffic load after Superblock implementation.
+- Spatial clustering and anisotropy of high betweenness centrality nodes [@kirkley2018]: Describes how clustered and non-uniformly distributed the expected traffic bottlenecks are.
+- Street orientation-order [@boeing2019]: Quantifies how grid-like each Superblock is.
+- Average circuity [@boeing2019a]: Measures the length increase of routes on the street network compared to straight-line distances.
+
 These metrics are calculated for the entire street network and for each Superblock
-individually.
+individually, providing insights into how the Superblock implementation might affect the
+overall city structure and local neighborhood characteristics.
 To facilitate further analysis, all of these metrics are included in the exportable
 GeoPackage file.
 

superblockify/_version.py

@@ -1,3 +1,3 @@
 """superblockify package version."""
 
-__version__ = "1.0.0rc11"
+__version__ = "1.0.0"