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The Morphology and Circuity of Walkable, Bikeable and Drivable Street Networks in Phnom Penh, Cambodia (Journal: Environment and Planning B: Urban Analytics and City Science)

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Street network analysis is a growing area in sustainable transportation research. Most academic papers on the topic have, so far, been concentrated in Europe and America, with less attention paid to rapidly growing cities in low-income nations. This is problematic because transportation networks are rapidly evolving in developing countries and the impacts of misguided transportation policies (including air pollution and road traffic casualties) are particularly acute. Metrics on the performance of street networks (SNs) could help inform policy. This paper uses the Python package OSMnx to analyze and evaluate SNs in twelve districts of Phnom Penh from OpenStreetMap. Results suggest that topological and geometric characteristics of SNs are more conducive to walking and biking in the central districts than in the peripheral districts. The central districts are also better connected to core network corridors. To promote sustainable urban mobility, new developments and street renewals should be incorporated facilities, services, and safety of walking and biking. Some policy implications are suggested for future designs of the Phnom Penh’s SNs to increase livability and sustainability
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Article
Urban Analytics and
City Science
The morphology and circuity
of walkable, bikeable, and
drivable street networks in
Phnom Penh, Cambodia
Yat Yen
Peking University, China; National Institute of Social Affairs, Cambodia
Pengjun Zhao
Peking University, China
Muhammad T Sohail
Xi’an Jiaotong University, China
Abstract
Street network analysis is a growing area in sustainable transportation research. Most academic
papers on the topic have, so far, been concentrated in Europe and America, with less attention
paid to rapidly growing cities in low income nations. This is problematic because transportation
networks are rapidly evolving in developing countries and the impacts of misguided transporta-
tion policies (including air pollution and road traffic casualties) are particularly acute. Metrics on
the performance of street networks could help inform policy. This paper uses the Python package
OSMnx to analyze and evaluate street networks in 12 districts of Phnom Penh from
OpenStreetMap. Results suggest that topological and geometric characteristics of street net-
works are more conducive to walking and biking in the central districts than in the peripheral
districts. The central districts are also better connected to core network corridors. To promote
sustainable urban mobility, new developments and street renewals should be incorporated facil-
ities, services, and safety of walking and biking. Some policy implications are suggested for future
designs of the Phnom Penh’s street networks to increase livability and sustainability.
Keywords
Street network, circuity analysis, urban morphology, OpenStreetMap, OSMnx
Corresponding author:
Yat Yen, Peking University, #5 Yiheyuan, Haidian district, Beijing 100871, China.
Email: yenyat_cambodia@pku.edu.cn
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DOI: 10.1177/2399808319857726
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Introduction
Making transportation systems more efficient and sustainable has become the main focus of
urban researchers and planners in recent years (Sharifi, 2019). Sustainable transport has par-
ticularly positive effects on the promotion of active travel and living (Mahmoudi et al., 2015;
Wey and Huang, 2018). A street that is designed for different modes of travel has a range of
mobility choices to reach the shortest destinations and provides safe and comfortable experi-
ences for pedestrians, cyclists, and transit riders (NACTO, 2016). However, such a design is
mostly neglected in the process of urban planning practice and research for the majority of
world cities (Lee, 2016). Streets that are optimized only for vehicular traffic will impose dangers
on all road users, mainly pedestrians and cyclists. For instance, World Health Organization
reported that pedestrians were the primary victims (between 55 and 70%) of road traffic deaths
in the developing world (WHO, 2015). A lack of proper urban design has therefore been tagged
as a key contributing factor for major public health and traffic accidents (Singh, 2016).
Numerous studies have been conducted to measure street networks (SNs) in Europe and
America, with less attention paid to rapidly growing cities in low income nations (Cao et al.,
2017). For example, Boeing (2018c) measured 27,000 urban SNs in the US cities, towns, and
urbanized areas. Likely, Karduni et al. (2016) analyzed SNs of 80 populated cities around
the world but did not include emerging cities in low income countries. Remarkably, some
researchers measure only SNs of car (Giacomin and Levinson, 2015), bike (Godwin and
Price, 2016), transit (Huang and Levinson, 2015), train (Caset et al., 2018), and travel time
(Cao et al., 2017), but the relative drivable street networks (DSNs) versus walkable street
networks (WSNs) have not been much explored (Boeing, 2018b). In this regard, Boeing
examined DSNs and WSNs in 40 US cities. Still, his study did not analyze bikeable street
networks (BSNs) together with DSNs and WSNs (Boeing, 2018b). Of course, BSNs are also
likely accessible for walking, but not all WNSs are bikeable, because some may include mid-
block cut-throughs, passageways between buildings, paths across parks, and other shortcuts
that are not bikeable or not allowed for biking. More importantly, different cities have
different street patterns. Some cities have irregular street patterns with short or crooked
streets while others have a hierarchical structure with regular, curvilinear, and orthogonal
patterns (Lee and Jung, 2018). Moreover, research highlights the lack of available datasets
for DSNs, WSNs, and BSNs in many cities, specifically in Cambodia. Some researchers also
admit to difficulties when doing research on SNs, such as (1) data limit and processing
constraints, (2) coding and programming skills required for data reproducibility (Karduni
et al., 2016), (3) excessive network simplification and inconsistency of definitions (Marshall
et al., 2018), and (4) the shortage of free and easy-to-use tools (Boeing, 2017).
Consequently, the extant paper examines the characteristics and accessibility of SNs in
Phnom Penh, a typical city with its distinctive properties. The study produced a network
dataset of the city and then used multiple metrics and the OSMnx toolkit to analyze spatial
features of DSNs, BSNs, and WSNs. The interpretation of various effects among the metrics
of the street configuration produced specific concepts for urban policy and design that could
re-adjust traffic issues and plans for better SNs.
Literature review
Definitions and measures of SNs
SNs are a backbone of urban transportation structures that organize human dynamics and
traffic flow. SNs also provide useful information that shapes commutes, travel behaviors,
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and location decisions of households and firms (Kang, 2017; Shatu and Yigitcanlar, 2018).
Theoretically, an analysis of SNs requires measuring both topological and geometric char-
acteristics (Spadon et al., 2018). The topological character explains the SNs’ connectivity,
centrality, and clustering, whereas the geometric character describes SNs’ distances, areas,
and densities (Boeing, 2018a).
Many studies used different tools and models to measure SNs. For example, some used
historical development patterns to measure SNs for bicycling and walking (Godwin and
Price, 2016), others employed circuity analysis to measure the travel time and distance (Cao
et al., 2017), and some others applied SPOT imagery to assess the morphology of SNs
(Mohd Nor et al., 2018). However, these studies used only some measures to analyze
SNs’ connectivity, centrality, circuity, traversal, topography, and evolution (Marshall
et al., 2018). Our study applied multiple metrics, such as closeness centrality, betweenness
centrality, average circuity, densities, nodes and edges, and street lengths, to measure SNs.
Using these approaches, the study more precisely captures multi-dimensional spatial fea-
tures of SNs.
SNs metrics are defined in some studies (Boeing, 2017; Ibnoulouafi and El Haziti, 2018;
Kirkley et al., 2018). Some definitions of the metrics associated with SNs are herewith
presented. Closeness centrality is a reciprocal of the sum of the distance from a node
(origin) to all reachable nodes (destinations) in SNs (He et al., 2018). It is necessary to
know the maximum closeness centrality values of the network in order to decide where to
place emergency service facilities that could enhance accessibility in case of disaster (Sharifi,
2019). Betweenness centrality is a prediction of how each SN link is populated as all possible
shortest paths pass through the node (Boeing, 2017). Edges are the interfaces between streets
and the adjoining buildings and plots. Whereas, the average street length is a linear proxy for
block size and specifies the network’s grain. Street density is a measurement of the total
street length divided by the areas in square kilometers (Boeing, 2018c). Additionally, circu-
ity, a ratio of shortest network distances to straight-line distances between origin and des-
tination, is a crucial element of network structure and transport efficiency that affects how
humans utilize urban space for settlement and travel (Ballou et al., 2002; Giacomin and
Levinson, 2015). Cities whose SNs have a low average circuity have more efficient transport
systems (Giacomin and Levinson, 2015).
The significance of SNs
The prominent roles of SNs in facilitating socioeconomic activities and environmental sus-
tainability have attracted much research efforts in this area (Marshall et al., 2018; Wang
et al., 2018). Specifically, each SNs’ metric interprets important information that has impli-
cations on urban morphology design and planning. For instance, different street attributes
promote higher walking and biking volumes (Sarkar et al., 2015). Also, a longer convex hull
with a maximum radius that covers a broader area and edges tends to boost social inter-
actions and cohesion in neighborhoods (Cooper et al., 2014). Furthermore, a higher density
of nodes could reduce traffic congestion (Gundleg ˚ard et al., 2016), promote connectivity
within neighborhood communities (He et al., 2018), and increase property value (Bielik
et al., 2018). Likewise, a vibrant edge that has active frontages and sidewalks, vegetation,
mixed land use, and traffic calming can turn streets into active boundaries and ensure active
living. Moreover, people tend to choose residential areas that offer less circuitous commutes
(Giacomin and Levinson, 2015) because the least average circuity is closer to the shortest
travel distance/time and indicates travel efficiency as well as the promotion of nonvehicular
Yen et al. 3
dependency (Cao et al., 2017). Although mounting scientific evidence proves the importance
of SNs, there has been scant information from such studies in Cambodia.
Method and materials
Study area
With a growth rate of 4.4% per year, Phnom Penh is the fastest growing city in Southeast
Asia. The city encapsulates 12 districts with a total area of 678.47 square kilometers and
1.8 million people in 2015 (Yen et al., 2017). The four central districts, Daun Penh, Chamkar
Mon, Tuol Kork, and Prampi Makara were jointly designed by French colonial urban
planners in the 1950s (Sub-Decree, 2015) and the peripheral districts were subsequently
integrated from neighboring provinces in 2000s (Figure 1).
The 1267 kilometers total road length in the capital is comprised of 718 kilometers
municipal, 455 kilometers rural, and 93 kilometers national roads. The capital also has
two railways lines—386 kilometers northern line running from Phnom Penh to
Figure 1. The map of the districts and the location of Phnom Penh.
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Battambang and Banteay Meanchey provinces, and the 264 kilometers southern line run-
ning from Phnom Penh to Sihanouk Ville.
Data acquisition
This study employed the OSMnx toolkit to download and analyze DSNs, BSNs, and WSNs
from the 2018 OSM data. OSMnx provides geospatial abilities and interacts directly with
OSM’s Nominatim and Overpass APIs by running the codes to extract SNs of interest
(Boeing, 2017). It can simplify and correct SNs’ topology and geometry automatically to
ensure that nodes accurately represent intersections and dead-ends. Moreover, it also can
calculate the shortest distance between nodes as well as other SN metrics (Figure 2). Users
can use OSMnx to query an area of interest by bounding box, address, point, polygon, or by
place names. The OSMnx also enables users to save SNs as GIS shapefiles. The researchers
could also use other tools, such as R package stplanr (a package for sustainable
transportation planning with R) to analyze spatial transportation data, with a focus on
origin–destination data (Lovelace and Ellison, 2018), and Cþþ library sDNA to predict
transportation flows (Cooper, 2017). The OSMnx packages and Jupyter Notebook were
installed in Anaconda v.4.5.11 (www.anaconda.com) to work with Python 3.7.0. To acquire
SN data for this study, first OSMnx buffered each geometry by 0.5 kilometers and then
downloaded the “nodes” and “edges” from the OSM within the buffer. Next, it constructed
SN graphs from these data, corrected the topology, and calculated metric and topological
measures for each SN (Figure 3). Finally, it saved each of SNs as shapefiles.
Data analysis
Data on the 12 districts in Phnom Penh were examined to analyze the differences of SNs.
Hypotheses were proposed to compare differences of average circuity of DSNs, BSNs, and
WSNs. A paired sample t-test was also used to confirm the statistical significance of differ-
ences between the three SNs’ circuities and to determine if the analysis supports the rejection
of the null hypothesis H
0
H0:ld¼lw;or ld¼lb;or lb¼lw
H1:ldlw;or ldlb;or lblw
Figure 2. A section of Phnom Penh’s DSNs, showing the shortest path (red line) between two nodes,
accounting for one-way streets, and the great-circle path (blue line).
Yen et al. 5
where ldis average circuity of DSNs, lwis average circuity of WSNs, and lbis average
circuity of BSNs.
The uratio of ldto lw(formula (1)), the ratio of ldto lb(formula (2)), and the ratio of
lbto lw(formula (3)) were formulated
u¼ld1
ðÞ
lb1
ðÞ (1)
u¼ld1
ðÞ
lw1
ðÞ (2)
u¼lb1
ðÞ
lw1
ðÞ (3)
The uratio was subtracted by 1 for each term because the minimum possible circuity is 1
(Boeing, 2018b; Giacomin and Levinson, 2015). Finally, for each district, we calculated the
effect size as Cohen’s d to measure whether or not lwwas greater than ldor vice versa, lw
was greater than lbor vice versa, and lbwas greater than ldor vice versa
dd=w¼ldlw
ðÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
nd1
ðÞ
d2
dþnw1
ðÞ
d2
w
nd1
ðÞþ
nw1
ðÞ
q(4)
Figure 3. The DSNs (dark gray), WSNs (light green), and BSNs (dark blue) between central district
(Prampi Makara) and peripheral district (Dangkao).
6EPB: Urban Analytics and City Science 0(0)
dd=b¼ldlb
ðÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
nd1
ðÞ
d2
dþnb1
ðÞ
d2
b
nd1
ðÞþ
nb1
ðÞ
q(5)
db=w¼lblw
ðÞ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
nb1
ðÞ
d2
bþnw1
ðÞ
d2
w
nb1
ðÞþ
nw1
ðÞ
q(6)
where dd=w;dd=b;db=ware the effect size of the difference between l
w
and l
d
,l
b
and l
d
, and
l
w
and l
b
, respectively. While n
d
,n
w
, and n
b
are numbers of sample sizes for DSNs, WSNs,
and BSNs, and d2
d;d2
w;d2
bare variances for DSNs, WSNs and BSNs. Cohen’s d divides the
mean difference in route circuity by the pooled standard deviation. If the effect of WSNs
exceeds the effect of DSNs on minimizing trip circuity, ldwould be greater than lw(i.e.
WSNs allow for more direct routes). Conversely, if the effect of DSNs exceeds the effect of
WSNs on minimizing trip circuity, lwwould be greater than ld(i.e. DSNs allow for more
direct routes).
Results
The edge densities in the central districts are higher than those (i.e. edge densities) in the
peripheries (Figure 4). Also, the densest edge density is found in the WSNs followed by the
edge density of the BSNs. The average circuities of DSNs versus WSNs significantly differ in
11 out of 12 districts while average circuities of DSNs versus BSNs statistically differ by
significant margins in all districts (Table 1). Only Prampi Makara district has average cir-
cuity of WSNs higher than that of average circuity of DSNs. Whereas the average circuities
of BSNs are statistically different from average circuities of WSNs, but the t-test did not
show a significant difference (p>0.05). Thus, the null hypothesis for WSNs versus BSNs
was rejected.
The mean distance of routes along DSNs (d
d
), BSNs (d
b
), and WSNs (d
d
) informs how
much distance can be saved between DSNs versus WSNs, DSNs versus BSNs, or WSNs
versus BSNs by taking a more direct mode of the trip. For example, the average WSNs in
Daun Penh are 42.92 meters shorter than the average DSNs. However, there is little differ-
ent mean distance between WSNs and BSNs. The differences of mean distance are corre-
lated with SN sizes and larger spatial extents of the city’s districts that require higher trip
distance. Among the four central districts, the total lengths of SNs in Prampi Makara are
the longest whereas SNs in peripheral districts, e.g. Pou Senchey, Dangkao, and Prek Phnov
have the longest total street lengths as they are larger areas.
The districts with orthogonal street grids are likely to have the least average circuity of
WSNs and BSNs while the peripheral districts with curvilinear residential SNs, such as in
Chroy Changvar, Chbar Ampov, Dangkao, and Prek Phnov, have the most circuitous
average for all SNs. The average DSNs are more circuitous than the average WSNs and
BSNs in most districts. For example, the averages of DSNs circuity in Daun Penh district
are 31.5 and 37.6% more circuitous than the average WSNs and BSNs, respectively. In
contrast, Prampi Makara has an average WSN of 0.6% more circuitous than average DSNs.
Comparatively, the average WSNs are less circuitous than the average BSNs in 7 out of 12
districts. The uratios and Cohen’s d present significantly different percentages of DSNs
versus WSNs and DSNs versus BSNs. The uand Cohen’s d of WSNs versus BSNs in five
districts are negatively different.
Yen et al. 7
Table 2 describes the different features of the three types of SNs. On average, the central
districts have a higher average street per node than the suburban areas, ranging from 3.12 to
3.37 streets for DSNs, 2.88 to 3.11 streets for WSNs, and 2.85 to 3.09 streets for BSNs. In the
central districts (e.g. Chamkar Mon), most intersections are four-way, whereas the peripheral
district (e.g. Dangkao) features a mix of mostly three-way and dead-ends (Figures 3 and 5).
Regarding node density metrics of DSNs, Prampi Makara and Tuol Kork have the highest
densities of 140.15 and 104.14 intersections/km
2
.Inversely,ChroyChangvarandPrekPhnov
have the lowest densities of just 10.30 and 15.26 intersections/km
2
, respectively. The node
Figure 4. The spatial distribution of the edge density of driving, biking, and walking SNs.
8EPB: Urban Analytics and City Science 0(0)
Table 1. The average circuity of DSNs, WSNs, and BSNs in Phnom Penh.
District
Area
(km
2
)l
d
l
w
l
b
d
d
d
w
d
b
P
d
P
w
P
b
d
d/w
d
d/b
d
b/w
u
d/w
u
d/b
u
b/w
Daun Penh 7.7 1.468 1.356 1.340 108.63 65.71 65.99 99.4 162.2 180.9 0.463*** 0.522*** 0.071 31.5% 37.6% 4.5%
Prampi Makara 2.2 1.288 1.290 1.286 88.10 72.49 76.15 49.6 67.8 60.7 0.007*** 0.008*** 0.017 0.6% 0.7% 1.3%
Tuol Kork 8.0 1.499 1.465 1.444 106.71 87.70 89.90 153.0 197.0 183.8 0.142*** 0.224*** 0.092 7.4% 12.4% 4.4%
Chamkar Mon 11.2 1.499 1.429 1.437 110.26 79.24 84.04 179.1 252.1 235.6 0.288*** 0.252*** 0.036 16.3% 14.1% 1.9%
Russey Keo 23.7 1.540 1.431 1.435 104.50 84.74 85.25 250.5 325.1 322.2 0.450*** 0.430*** 0.016 25.3% 24.3% 0.8%
Chroy Changvar 85.4 2.106 1.919 1.918 158.64 124.08 125.24 187.2 253.4 251.6 0.773*** 0.767*** 0.004 20.3% 20.5% 0.1%
Sen Sok 53.7 1.498 1.419 1.424 94.00 79.75 79.79 584.3 771.8 768.6 0.327*** 0.302*** 0.023 18.9% 17.5% 1.2%
Pou Senchey 147.9 1.588 1.520 1.516 99.22 90.33 90.56 887.9 1251.9 1250.0 0.280*** 0.293*** 0.018 13.0% 13.9% 0.8%
Mean Chey 28.6 1.424 1.364 1.368 86.30 76.75 77.62 319.7 423.5 420.4 0.249*** 0.229*** 0.018 16.6% 15.3% 1.1%
Chbar Ampov 86.7 2.018 1.882 1.896 134.47 119.83 120.68 347.5 488.2 483.9 0.562*** 0.498*** 0.062 15.4% 13.6% 1.6%
Dangkao 114.1 1.801 1.726 1.728 111.79 99.62 101.26 550.9 790.2 784.4 0.311*** 0.299*** 0.009 10.4% 10.1% 0.3%
Prek Phnov 115.3 1.933 1.821 1.825 119.77 113.76 114.36 283.6 457.9 459.0 0.463*** 0.441*** 0.018 13.6% 13.1% 0.5%
Note: ld;lw;lbare average circuity of DSNs, WSNs, and BSNs. d
d
,d
w
,d
b
¼mean distance (meters) of routes along DNSs, WSNs, BSNs. Pd,Pw,Pbare total street length
(kilometers) of DSNs, WSNs, and BSNs. Cohen’s dd=w;dd=w;dd=ware the effect size of the difference between l
w
and l
d
,l
b
and l
d
, and l
w
and l
b
, respectively. The urepresents
how much (%) l
d
exceeds l
w
, or how much (%) l
d
exceeds l
b
, and how much (%) l
b
exceeds l
w
. *** indicates a statistically significant difference between l
w
,l
b
, and l
d
at the p<.001.
Yen et al. 9
densities for WSNs are the highest in Prampi Makara (248.41 intersections/km
2
) followed by
Daun Penh (193.45 intersections/km
2
). Whereas, Chroy Changvar and Prek Phnov remain the
least with 18.48 and 25.70 intersections/km
2
. It is not much different for the node densities of
BSNs, Chroy Changvar (18.27 intersections/km
2
) and Prek Phnov (25.63 intersections/km
2
)
are still the least ones while Prampi Makara remains the district with the highest density of
213.37 intersections/km
2
. The street densities are high for those SNs in the central districts
although the different rates are not large. In addition, Prampi Makara has the highest linear
kilometers of physical street/km
2
followed by Tuol Kork and Chamkar Mon. In contrast,
Chroy Changvar and Chbar Ampov have the least linear kilometers of physical street/km
2
.
As a proxy of the block size, Chroy Changvar has the most extended average street segment
length for all SNs, followed by Chbar Ampov and Prek Phnov because some parts of these
districts are covered by riversides, catchments, ponds, and lakes.
Moreover, the spatial distribution of closeness centrality and betweenness centrality for
each SN shows the relative importance of each node (Figure 5 and Table 2). The critical
nodes are concentrated in the center of these SNs due to their grid-like orthogonality.
The closeness centrality of each SN is very small and the same for all districts (0.1%) but
Figure 5. Distribution pattern of closeness and betweenness centrality of DSNs in the central (Prampi
Makara (a) and (b)) and outer districts (Dangkao (c) and (d)) of Phnom Penh. The darker the color, the lower
the closeness and betweenness centrality.
10 EPB: Urban Analytics and City Science 0(0)
the betweenness centrality of all shortest paths passing through an average node in the
central districts is lower than the peripheral districts. For instance, Prampi Makara has
3.4, 2.2, and 2.6% of betweenness, whereas Prek Phnov has 0.8, 0.7, and 0.7% of between-
ness for DSNs, WSNs, and BSNs, respectively.
Discussion
Analysis of SNs’ characteristics
The findings demonstrate significantly different characteristics of SNs in the central and
peripheral districts. The one-way streets and densities of nodes, edges, and intersection are
the factors affecting average circuity between the city center and its peripheral areas.
Geometrically, the shortest-average distance and higher edge and node densities make the
central districts more accessible for mobility and thereby they are potential hubs to connect
the districts to the core SN corridors. The important nodes in the peripheral districts,
e.g. Dangkao district, are critical chokepoints linking one side of the SNs to the others.
Table 2. The measures of three types of SNs in Phnom Penh.
Types Measure
District
Daun
Penh
Prampi
Makara
Tuol
Kork
Chamkar
Mon
Russey
Keo
Chroy
Changvar
Sen
Sok
Pou
Senchey
Mean
Chey
Chbar
Ampov Dangkao
Prek
Phnov
DSNs n 573 356 982 1068 1818 884 4695 6970 2946 2004 3833 2006
g68.46 140.15 104.14 86.26 75.27 10.30 73.25 37.39 73.75 26.93 29.05 15.26
q66.07 136.61 90.99 76.81 56.64 7.80 55.77 27.22 53.45 19.49 20.92 10.02
c(km) 38.1 38.9 35.0 33.7 24.6 5.2 22.3 12.8 19.6 12.6 11.5 6.6
1(km) 11.9 19.5 16.2 14.5 10.4 2.2 9.1 4.8 8.0 4.7 4.2 2.2
d(km) 108.63 88.10 106.71 110.26 104.50 158.64 94.00 99.22 86.30 134.47 111.79 119.77
a3.32 3.37 3.03 3.12 2.66 2.68 2.68 2.59 2.56 2.58 2.59 2.4
Ϛ0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Ϭ0.029 0.034 0.021 0.017 0.014 0.030 0.008 0.006 0.012 0.002 0.008 0.029
WSNs n 1746 631 1602 2220 3091 1586 7640 11052 4460 3228 6354 3379
g193.45 248.41 169.89 179.31 127.97 18.48 119.20 58.69 111.65 43.38 48.16 25.70
q162.09 225.18 141.04 148.94 88.72 13.31 85.17 41.28 78.41 30.60 33.57 17.14
c(km) 35.7 53.0 41.6 40.5 26.9 5.9 23.9 13.2 20.9 13.08 11.9 6.9
1(km) 18.0 26.7 20.9 20.4 13.5 3.0 12.0 6.6 10.6 6.6 6.0 3.5
d(km) 65.71 72.49 87.70 79.24 84.74 124.08 79.75 90.33 76.75 119.83 99.62 113.76
a2.89 3.11 2.88 2.90 2.5 2.58 2.55 0.52 2.5 2.52 2.51 2.41
Ϛ0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Ϭ0.013 0.022 0.015 0.011 0.012 0.022 0.007 0.005 0.010 0.013 0.007 0.023
BSNs n 1932 542 1481 1972 3048 1568 7611 11016 4391 3177 6229 3369
g204.52 213.37 157.06 159.28 126.19 18.27 116.84 58.50 109.92 42.69 47.21 25.63
q171.18 190.93 128.53 131.58 87.40 13.09 83.37 41.12 76.83 30.16 32.74 17.10
c(km) 38.1 38.9 35.0 33.7 24.6 5.2 22.3 12.8 19.6 12.6 11.5 6.6
1(km) 19.1 23.9 19.5 19.0 13.3 2.9 11.8 6.6 10.5 6.5 5.9 3.5
d(km) 65.99 76.15 89.90 84.04 85.25 125.24 79.79 90.56 77.62 120.68 101.26 114.36
a2.89 3.09 2.85 2.89 2.499 2.57 2.55 2.52 2.494 2.52 2.5 2.41
Ϛ0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.01 0.001
Ϭ0.012 0.026 0.017 0.013 0.013 0.023 0.007 0.005 0.011 0.014 0.007 0.024
BSNs: bikeable street networks; DSNs: drivable street networks; WSNs: walkable street networks.
Note: Number of nodes (n), node density (g), intersection density (q), edge density (c), street density (1), average street
segment length (d), average street per node (a), closeness centrality (Ϛ), and betweenness centrality (Ϭ).
Yen et al. 11
This phenomenon makes the SNs in the peripheral districts more prone to disruption if the
most important nodes fail (e.g. due to a traffic jam and flood) than those in the central districts
under similar conditions (Witte et al., 2012). This happened in New York City where the
surrounding highways that have high link betweenness centralities are located in floodplains
(Kermanshah and Derrible, 2017). Besides, the primary street systems may run at crosscutting
diagonals that provide direct routes across the districts where local or small streets could not
match. Geographically, Phnom Penh is located at the confluence of four rivers where the
connections of the western districts to eastern districts pass through bridges and via ferries.
Thus, the effect of automobile-only DSNs in these districts exceeds the effects of WSNs and
BSNs on route directness. Some one-way routes in DSNs may be accessible to pedestrians and
cyclists in both directions. In contrast, WSNs and BSNs may be inaccessible to vehicular
drivers while some streets and bridges may be unavailable to pedestrians and cyclists. The
different topographies, transport technologies, and design paradigms have affected the urban
form and SNs in this city where more newly constructed parts of SNs in the peripheral
districts are laid out less efficiently than older parts in the central districts. The future designs
and development of SNs in this capital should pay more attention to the peripheral districts.
Meanwhile, it is crucial to differentiate street lanes for vehicular, cycling, and walking traffic
so as to protect pedestrians and cyclists from vehicular accidents.
Phnom Penh is like other cities in Southeast Asia (ASEAN) whose designs and architec-
ture were influenced by European styles during colonization, for example Manila (1570 by
Spanish), Jakarta (1619 by Dutch), Singapore (1819 by British), Yangon (1834 by British),
Ho Chi Minh (1859 by French), and Phnom Penh (1866 by French) (Dick and Rimmer,
1998). However, the issues faced by ASEAN member states are different and yet make
ASEAN a competitive community through partnerships. Singapore is the only country
that has the most cost-efficient public transportation networks where its people can have
access to different modes of travel, including public buses, taxis, trains, monorails, subways,
and expressways (Wang et al., 2016). In contrast, Cambodian people have limited access to
the necessary services and infrastructure, living far distances from work and services, and
are at the highest risk of traffic accidents and flooding (World Bank, 2017). The first public
buses were put into use in 2014; and in 2018, only 13 bus lines were available on the main
streets in the city. Lack of alternative modes of travel forces residents to use private trans-
portation. The city’s registered vehicle fleet volume reached 442,972 cars and 1,601,451
motorbikes in 2016. Some SNs in Thailand are likely the same as in SNs in Cambodia.
Although SNs in Thailand have been extensively developed since the post-Second World
War era, accessibility of SNs to walking and cycling is also a key issue (Pongprasert and
Kubota, 2017). Unlike other countries in Asia, e.g. China, Korea, Japan, and India where
remarkable research on SNs has been done, such research in ASEAN is very scant. Many
studies have been conducted on an individual network basis, such as walkable streets for
tourists (Henderson, 2018), a relationship between land use and road network connectivity
(Patarasuk, 2013), metro network (Fesselmeyer and Liu, 2018), network connectivity
between old and new urban areas (Said and Mohamad, 2017), and modes of transport
networks (Andong and Sajor, 2017). Thus, SNs remain an important research direction
for other cities in the region.
Policy implications
Our results suggest that different strategies should apply to promote walking and cycling
accessibility in Phnom Penh to reduce vehicular dependency. The design of WSNs and BSNs
must provide multiple routes with continuous clear paths to reach destinations at a shortest-
12 EPB: Urban Analytics and City Science 0(0)
distance. Walkability and bikeability increase as more destinations can be reached along
routes with better safer facilities to support walking and cycling (Lowry et al., 2012; Rahul
and Verma, 2018). For instance, a study on the buffered two-way bike lane in Pennsylvania
Avenue, Washington, DC found a 250% increase in cycling levels during peak commute
hours two years after the installation of bike facilities (Goodno et al., 2013). To ensure
safety for pedestrians and cyclists, it is crucial to provide physically separated, protected
walking and cycling infrastructure on major streets with high-volume vehicular traffic
(Pucher and Buehler, 2017). Although SNs in the central districts of this study show
better accessibility than those in the peripheral districts, neither central nor peripheral
districts have proper designs and facilities to separate the pedestrians and cyclists’ routes
from vehicular traffic. It is important to re-design some streets by replacing some two-way
roads with roads that are one-way for cars and two-way for other modes. Preventing
hawkers from illegal parking and occupying the street would enlarge street space and
improve safe accessibility. Also, providing sufficient infrastructure, including bicycle
lanes, sidewalks, pedestrian crossings, pedestrian ramps, cycle tracks, signage and wayfind-
ing, traffic calming at neighborhood streets, greenery streets, and improvement of safety at
edges and intersections, could increase levels of walking and cycling (Buehler and Dill,
2016). However, such designs and facilities have not been made available in Phnom Penh
to date, and traffic accidents and congestion have thereby become the worst issue in the
capital. It is an immediate need that the government should focus on improving SNs as a
matter of urgency and shift from the vehicular dependency to an active mode of transpor-
tation (i.e. walking and cycling). Furthermore, poorly planned roadways and the rapid
expansion of urbanized areas in the peripheral districts have a huge negative impact on
street development in these districts. To avoid urban sprawl and disruptive SNs, practical
land use and appropriate land use development control are necessary. Proper preparation
for a street design manual is also necessary to guide newly constructed streets so as to plan
and design new streets that will provide more accessibility to walking and cycling in the city.
Conclusion
This study provides an in-depth analysis of drivable street networks (DSNs), walkable street
networks (WSNs), and bikeable street networks (BSNs) of Phnom Penh. The topological
and geometric characteristics of SNs in the central districts are more accessible to walking
and biking than the peripheral districts. Therefore, WSNs and BSNs in the central districts
tend to allow for more direct routes than DSNs. The important nodes in the peripheral
districts are critical chokepoints linking one side of the SNs to the others. These SNs are
more prone to disruption if the most important nodes fail.
To promote sustainable urban mobility, future designs of street layouts that equip with
WSNs and BSNs infrastructure should be of priority. Urban residents prefer living
and traveling in a place with the shortest-distance of SNs to the destinations, safe and
convenient navigation, and accessibility to services. Planners should incorporate facilities,
services, and safety of walking and biking in the peripheral districts, such as Mean Chey,
Pou Senchey, and Dangkao districts, where there are textile factories and workers with
bustling traffic. The improvement of SNs in these districts may reduce travel demand into
the central districts and catalyze out-migration to suburban areas. This will significantly
reduce traffic congestion, high population density, and promote sustainable urban
development.
This study suggests that using a network approach could allow planners to estimate the
effects of making changes to the network and thus yield cost savings. However, this is a
Yen et al. 13
local-level study which investigated only Phnom Penh and is limited because SNs in other
locations are varied by urban types, technologies, time, and designs. Further research will
need to be sensitive to these differences. Future research could use OSMnx and
OpenStreetMap data to compare different times of SNs at the national level or be expanded
to gain more insights about regional city planning in Southeast Asia or globally. This study
also only highlights the potential and need for further studies on SNs to answer the ques-
tions of “where to be built” and “what to be built” in the Phnom Penh, in order to improve
accessible and safe SNs for pedestrians and cyclists.
The extant study outlined a method for extracting, analyzing, and visualizing spatial
distribution and SNs to create an evidence base for planning specific routes that are relevant
to the local contexts. With the help of OSMnx and code chunks (see online supplementary
material), future studies could use this method to analyze urban morphology and SNs for
other cities and reproduce datasets for SNs. Although OSMnx allows users to download
different types of SNs, it is unable to extract different years of SNs from OSM data. This
may pose another challenge for researchers who use OSMnx to analyze and compare the
developments of SNs in different time series.
Authors’ note
Yat Yen is now affiliated with Center for Khmer Studies, Cambodia.
Acknowledgments
Authors sincerely thank Prof. Geoff Boeing who develops OSMnx that is a valuable and convenient
research tool to extract the data from OpenStreetMap for this study. The authors also gratefully
thank Leo Atwood, a Canadian English language editor and Dr Earl Bailey for proofreading and
editing this manuscript. The authors unforgettably express sincere gratitude to the Editor Dr Daniel
Arribas-bel and the anonymous reviewers for their constructive comments on earlier versions of
this manuscript.
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) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: This research was supported by Boya Post-doctoral Research Program,
Peking University and by Senior Fellowship Program at Center for Khmer Studies, Cambodia.
ORCID iD
Yat Yen https://orcid.org/0000-0003-0156-453X
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