Mapping Urban Accessibility in Data Scarce Contexts Using Space Syntax and Location-Based Methods
Mapping Urban Accessibility in Data Scarce Contexts Using Space Syntax and Location-Based Methods
Jose Morales 0
Johannes Flacke 0
Javier Morales 0
Jaap Zevenbergen 0
0 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente , P.O. Box 217, 7500 AE Enschede , The Netherlands
Data scarcity is still a common barrier to adequately understanding urban access in Global South countries. Widely used location-based methods address the traditional definition of accessibility as the easiness to reach land-uses by means of available mobility modes. Space Syntax instead analyses accessibility as network centrality focusing only on the topological and geometric properties of urban layouts, making it comparatively less data-intense. However, the interpretation of its outputs is limited to its own theory. Knowledge is missing on how such metrics are comparable to the metrics produced by location-based methods. The objective of the research was to compare both approaches for mapping urban accessibility in two cities in Guatemala. Our hypothesis tested the assumption that Space Syntax metrics could consistently reflect accessibility conditions that so far have only been measured by location-based methods. We proposed an approach using volunteered geo-information and produced accessibility maps following both approaches that were then compared using Pearson correlations. Space Syntax metrics at low and high radii are consistently correlated with location-based access to land uses that reflect location quality at neighbourhood and city-wide scale correspondingly. Space Syntax metrics at lower radii reflect time-based access restrictions either posed in the location-based analyses or by reduced accessibility by public transport. The hypothesis acceptance, p < 0.01, expands the scope of accessibility knowledge derivable from limited data availability using Space Syntax, which is relevant for its applicability in data-scarce contexts by planners and researchers in the Global South. Rather than replacing location-based methods Space Syntax offers an important complementary measure to geographical accessibility. This having been said, Space Syntax could contribute to early-stage planning by gaining overall insights into patterns of urban access.
Understanding urban accessibility is fundamental for land use and transport planning
(Curtis and Scheurer 2010; Geurs and Van Wee 2004; Curl et al. 2011)
, as it is one of
the key aspects for agglomeration economies, economic growth, and quality of life
Ahlström et al. 2011
Kourtit et al. 2015
). Two conceptions of urban
accessibility can be distinguished. Geographic accessibility is the most common one
and is defined as the opportunity at origin to reach a destination, or vice-versa, given
the impedance between both locations
(Curl et al. 2011; Handy and Niemeier 1997;
Batty 2009; Geurs and Van Wee 2004; Ingram 1971; Albacete et al. 2015)
combined effect of land use distribution and infrastructure components at a given
location determines geographic accessibility (Geurs and van Eck 2001). Geometric or
general accessibility, on the other hand, is concerned with network centrality and
focuses on the topological, metric and geometric properties of urban layouts
2010; Hillier et al. 2010; Bafna 2003; Batty 2004)
Two methodological approaches correspond to the two concepts of access.
Locationbased measurements have been the preferred methods to analyse geographic
(Geurs and van Eck 2001; Curl et al. 2011)
. In turn, Space Syntax (SSx) is a set of
theories and methods with long-standing development whose purpose is to analyse
(Webster 2010; Karimi 2012; Hillier et al. 1976)
. The availability
of geographic data (e.g. land use, road and public transport networks), the easiness of
interpretation and applicability of geographic information systems (GIS) have facilitated
implementing location-based methods for transport planning purposes. However, the
scarcity of official data and capacities for processing the same is still an important barrier
in Global South countries (Yeh 1991;
Ahlström et al. 2011
) such as
Guatemala. Common problems are incomplete or outdated data sets as resources might
not be available for periodic collection and maintenance.
Alternative sources of information such as volunteered geographical information
(VGI) might be potentially useful when dealing with scarcity of official data
et al. 2015)
, jointly with considering a geometric accessibility concept. The SSx
method is less data-intense than traditional location-based methods. Only a
representation of a roads network is needed for the analysis. Previous work has already reported
associations between SSx metrics with relevant urban phenomena: flows of people
(Hajrasouliha and Yin 2015)
, land use and construction density
(Kim and Sohn 2002;
Hillier et al. 2000; van Nes et al. 2011; Hillier et al. 2010)
and real estate values
(Matthews and Turnbull 2007; Netzell 2012)
SSx has also been debated regarding its dual analytical approach
(Ratti 2004; Hillier
and Penn 2004; Porta et al. 2006)
emphasized the problem of
mathematically relating the topological-based measurements with the intuitive geographic ones
(e.g. distance or time) and proposed an analytical framework to reconcile SSx with
metric information. The SSx approach has attempted to prove itself a complementary
tool to aid planners and researchers in accessibility studies, particularly in data-scarce
contexts. However, the interpretation of its outputs remains limited to its own theory
and knowledge is missing on how such metrics are comparable to the measurements
produced by location-based methods. These observations restrict its applicability as an
analytical approach when data availability is limited.
The objective of this research was to compare a geographical and a geometrical
approach for mapping urban accessibility. Our hypothesis tested the assumption that
Space Syntax metrics could consistently reflect urban access conditions that so far have
only been measured by location-based methods. By testing this hypothesis we attempted
to contribute in empirically bridging both approaches and expanding the scope of
knowledge derivable from SSx. This is relevant for planning practice as regards the
applicability of available methods to address accessibility-related planning tasks in the
context of Global South cities with data-challenging environments. Two cities in
Guatemala were studied in order to examine the applicability of both approaches in different
heterogeneous and fragmented contexts. We developed a methodological framework for
analysing accessibility using SSx and location-based methods. This included a tailored
approach that uses VGI to mitigate unavailability of official data. We computed
locationbased access per mode of transport to key land uses that are relevant in planning practice
and are commonly associated with urban-economic dynamics. We further derived two
SSx metrics at the road-level at various spatial scales. Finally, the results from both
approaches were compared using Pearson correlation. The strength and significance
(p < 0.01) were evaluated. We elaborated on how geometric accessibility measurements
provided information that was comparable to geographic access to various land-uses per
mode of transport, its limitations and its applicability in practice.
The remainder of this paper is organised as follows: section 2 introduces the
location-based and SSx-based accessibility measurements used in this research.
Section 3 describes the methodological framework and introduces the case study areas.
Section 4 presents the results and discussion. Finally, section 5 addresses the
Location-Based Methods and Space Syntax
Location-based methods are widely used in research and practice
Wegener and Fürst 2004; Handy and Niemeier 1997; Geurs and Van Wee 2004;
Albacete et al. 2015)
. They aim to analyse accessibility considering four
(Geurs and van Eck 2001, page 35)
: (1) mobility infrastructure (i.e. roads,
public space, public transport), (2) land-use location, (3) temporal conditions of
the previous two, such as variability of travel-times and available land uses during
the course of the day or week and (4) personal-level characteristics and
restrictions. A plausible accessibility model would attempt to address these aspects as far
as possible in accordance with its purpose. However, it will be limited by the
availability of geographic data.
Three commonly used location-based measurements are: (1) impedance to closest
facility, (2) cumulative opportunity, and (3) potential accessibility. The first analyses
proximity following the criteria of shortest trip where impedance is commonly defined
by travel time (per mobility mode), distance or cost. Cumulative opportunity measures
the number of reachable attractions within a given impedance threshold and takes the
form of Eq. 1.
Σ M j; i f di j ≤ R
0; i f di j > R
Where A is the access at origin i; M is the size of the attraction at destination j; d is the
impedance between i and j; and R is the radius restriction. The potential accessibility
can be traced back to
Stewart and Warntz (1958)
. It accounts for the
size of attraction (e.g. number of jobs) and the effect of distance on the interaction
probability between origin and destination. Such effect is commonly named distance
decay. The measurement takes the form of Eq. 2.
∑ M j αexp −β*di j
Where M is equal to the size of the attraction at j; and α and β are constant parameters
that determine the distance decay.
These three measurements are simple and less data-intense compared to other
location-based measurements such as those based on balancing factors and derived
from time-space geography
(Geurs and van Eck 2001; Curl et al. 2011)
components of Eqs. 1 and 2 can be adapted to data availability. For example, impedance can
be measured in planar or network distance, time, or cost. Although planar or even
network distance could be used if data is scarce, real mobility conditions are
represented better when using travel time or cost per mode of transport. The size of attraction
‘M’ in both equations could simply represent the number of facilities available (e.g.
number of public spaces). Even though a more realistic representation could for
instance to include floor area.
Limitations of these measurements have been described by Geurs and van Eck
(2001). A cumulative opportunity does not distinguish impedance or attraction
size differences between the various destinations reached within the fixed
threshold. These limitations are overcome by the potential accessibility measurement.
However, decay parameters should be calibrated per mobility mode and trip
purpose, which is more data-demanding. Results are less intuitive to interpret,
although acceptable to non-specialists. Some drawbacks of the potential
accessibility measurement are: influence of self-potential, attraction within origin zone;
no distinction between matching types of attraction and individual preferences;
only addressing of the spatial distribution of attraction supply, not the demand of
those. Extensions of the basic gravity model have addressed these drawbacks at a
cost of more data needs and interpretability.
Space Syntax (SSx)
SSx is a network analytical formalism to analyse a type of accessibility that also
has an economic significance
Hillier et al. (1993)
access type as the easiness to move through and to places given the spatial
arrangement of urban layouts, which has shown to be correlated with flows and
attraction of movement. Urban economies are tightly linked to these dynamics
as certain land uses benefit from these flows based on a maximum profitability
. Positive correlations between SSx with real estate
values and construction density support such a relation
Turnbull 2007; Kim and Sohn 2002; Netzell 2012)
. It follows that we can
expect a positive correlation between accessibility as analysed in SSx and
location-based accessibility to various land uses that follow an economic
rationale, or service type of activities where the purpose is to be reachable.
Sharing similar grounds with SSx is the Multiple Centrality Assessment
(Porta et al. 2005)
. The main difference between the two is
that SSx analyses are computed using a dual graph, while MCA is based on
a primal one. Opposite to the dual graph, in the primal approach intersections
are treated as nodes and streets as edges. While SSx is known for pioneering in
the studies of network centrality applied to cities, MCA presents itself as an
enhanced method with recent evidence of its capacity to correlate with location
of economic activities
(Porta et al. 2010; Porta et al. 2012)
. However, in our
research we consider it appropriate to implement the SSx approach as it
benefits from a notably larger body of literature empirically supporting its
applicability in various urban studies, in planning and design processes and
with respect to the availability of applications for direct implementation within
GIS. Following Law (2017), metrics analysed at the street level via a dual
approach would be adequate to compare with accessibility metrics that are
derived from travel times and location of places along the street, not at
SSx analyses over road centre-lines are done using a segment angular
analysis (SAA) technique
(Turner 2007; Hillier and Iida 2005)
. It is a geometric
weighting method that works as an impedance parameter based on the idea that
persons seek to minimize their angular deviation when choosing trip routes
(Dalton 2003). Implicitly SSA accounts for the continuity of road segments, but
without incurring in an explicit generalization process (network simplification)
such as the Bstreet-name approach^ or the Bcontinuity negotiation algorithm^
(Jiang and Claramunt 2002; Porta et al. 2006)
Two main variables are analysed: integration and choice. Integration is
equivalent to network closeness, and choice to network betweenness
Porta et al. 2005)
. Integration measures how close each segment is to any other
segment in the network. Choice measures the cumulative number of times that
each segment is used in shortest trips from every segment towards every other
segment. Impedance in SAA is based on angular deviation between segments,
unlike the measurements of time or distance in geographic access. Thus, angular
integration at any given x segment takes the form of Eq. 3,
∑in¼1Dθðx; iÞ −1
where n is equal to the number of segments in the system, and Dθ(x, i) is the angular
depth between x segment and any other segment in the network, i. Depth indicates the
cumulative angular deviation. Angular choice is expressed in Eq. 4,
∑in¼1∑nj¼1σði; x; jÞ
where σ(i , x , j) = ‘1’, once x is used to go from i to j, else = ‘0’ and being i ≠ x ≠ j.
Hillier et al. (2012)
suggest a normalization procedure for integration (NAIN) and
choice (NACH) to a scale ranging from −3 to 3. While the normalizing choice is highly
recommended, normalising both values allows comparing the results between segments
within a city, and with other cities. Yet,
Hillier et al. (2012)
report some inconsistencies
about the use of NAIN.
In SSx terminology global integration and choice measurements are carried out at
city-wide spatial scales. Local integration and choice values are analysed by
introducing metricized restriction radii
(Hillier et al. 2010)
. High local integration values are
associated with walkable areas that have dense and consolidated networks. High local
choice values are associated with streets that serve to connect the neighbourhood-level
areas to higher-hierarchy roads. Various integration values at increasing radii are argued
to be empirically correlated with various types of movement patterns
(Hillier et al.
1993; Hillier 1996; Hillier 2009; Penn 2003)
Case Study Areas
Our case study cities are in Guatemala, Central America: Guatemala City (GC) and
Quetzaltenango (QT). As in other countries in Latin America, the country has a colonial
heritage in planning tradition
(Ford 1996; Griffin and Ford 1980)
. This is reflected in
historic gridiron networks and common Global South problems
Glebbeek and Koonings 2015)
such as: heterogeneous and fragmented urban
development, deteriorated historic cores, top-down, but weak planning practice and
congestionrelated problems due to the uneven and unplanned horizontal expansion and centralized
economic land uses. Both cities have expanded from an historic core, starting with
planned expansions, and then moved towards unplanned peripheral developments
following the main infrastructure (see Fig. 1). The first planned expansions are
associated with current location of the core-business district (CBD). However, they
differ significantly in size and stage of urban development, reflected in different streets
configurations and ongoing economic dynamics. These differences made these cities
adequate to the test applicability of our approach in different urban setups.
GC is the country’s capital located in the central region. It accommodates around
26% of the country’s population. It extends over 996 km2 within the municipal
administrative boundary, excluding the conurbation areas in contiguous
municipalities. Horizontal expansion is mainly shaped by topographic conditions. A
segment of a non-finished peripheral ring connects the foundational core with the west
and south-west areas. Current expansion mostly occurs in the south-eastern,
southwestern and western areas, outside the administrative boundary. These areas are
nurtured by the main infrastructure and the intra-regional CA-1 road.
QT is the second most important city, located in the west of the country. It
accommodates around 5% of the population in a minor extension of 120 km2.
Location of important infrastructure such as the airport, a peripheral road segment
combined with topographical conditions influence current expansions mostly towards
the northern and north-western areas. Still, further than the foundational core and the
first expansion, a slow infill process is observable in the rest of the urban area.
Data Collection, VGI Data and Pre-Processing
Table 1 describes the data per accessibility approach that were used for the
analyses. Official data were collected during fieldwork in the period of August
2014 to March 2015. The main problems with the data obtained from official
institutions were: lack of up-to-dateness and incomplete geographical coverage.
Data sets in GC only cover the administrative boundary, even though the
functional city extends beyond those. Data from the contiguous municipalities do not
exist. Therefore, we implemented a tailored approach that included extracting and
pre-processing various sources of VGI.
Topological inconsistencies were the main problem when using OSM network
(Cooper and Chiaradia 2015; Cooper 2014; Gil 2015)
. Pre-processing involved
the following steps: filtering out all the roads where vehicles are not allowed
following OSM tags convention, planarizing the networks, except at overpass
locations, removing duplicated features and detecting and correcting unconnected
road segments. We simplified the networks using the Douglas-Peucker algorithm
with an offset tolerance of 3 m for the SSx analyses. Then the simplified networks
were fragmented at each vertex.
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O J L X U C X L R P M H S C
We built time-based network models for private-vehicle (PRIV) and public-transport
mobility (PUB) for each city. First we examined for the correct categorical road
classification based on OSM tags convention (e.g. motorway, primary, secondary,
residential). A few important roads were found misclassified and were corrected. Speed
limits were consulted with the respective authorities and added to the road segments
according to their classification. Then we used Waze
in GC and field
observations in QT to calibrate final travel-times. The PUB models in both cities
incorporate the roads as pedestrian networks. Travel-time calibration was carried out
in consultation with local planners and experts.
In GC, origin-destination (OD) matrices per mode of transport (PRIV and PUB)
during peak hours (6:00–9:00 am) were available for the year 2005. Each matrix
contains 173 rows and columns, corresponding to 173 Traffic Analysis Zones (TAZ).
Even though trip volumes in these data are outdated, we assumed they reflect overall
mobility patterns that have not changed significantly. It means that the major attractors
of morning trips, central locations of jobs and commerce, are the same up to date. In
QT, OD data are more recent (2014) but drastically less detailed as each TAZ represents
one of the 12 postal zones. Only the total number of trips attracted and generated during
peak hours is available. Overall, the use of OD data was an alternative solution to
overcome unavailable data on job locations. The total number of trips attracted was
normalised per TAZ area and used as a proxy variable to indicate the density of job
Table 1 shows that VGI was the main data source to compensate incomplete
landuse data, extracted as points-of-interest (POIs). Still, exhaustive pre-processing was
needed to use these data. In OSM most of the land uses were found as POIs, but in
some cases they are digitised as polygons, or both. POIs of restaurants or banks within
a large-scale mall (as POI or polygon) were simultaneously digitized, so they had to be
detected and removed to avoid double counting. OSM conventions are not used
consistently and the completeness of the data sets relies on the active contributors.
Thus, other sources (GoogleMaps and Wikimapia) were used to cross-check and
complete each land-use. Official land-use data was used mostly as a secondary
reference. The final compiled datasets with POI locations were discussed with local
planners to validate their use.
Implementing Accessibility Analyses
Figure 2 shows our methodological framework to map urban accessibility using a
geographical and a geometrical approach. At a first glance, differences in data
requirements are noticeable in each approach. In the geographical approach we started with the
analysis of access to different land uses, by grouping them based on two sets of
variables: macro- and micro-location. Macro-location addresses all those variables
where, due to their characteristics (relevance and scale), people are more willing to
overcome impedance. Micro-location addresses all locations with characteristics related
to neighbourhood scale; thus, people are relatively less willing to overcome impedance.
During the fieldwork we validated the relevance of these variables by means of a
workshop with local experts in planning and real estate markets. Participants also
ranked the variables on the basis of the relative importance of each variable to the
local land market.
We generated a hexagonal tessellation for each study area. A hexagonal shape was
chosen for the following advantages: (1) spatial sampling performing slightly better
when using geostatistical techniques compared to a grid sampling
(Birch et al. 2007;
Condat et al. 2005)
, if the analyses were to be used to investigate their relation with
other socio-economic dynamics; (2) more symmetric nearest neighbourhoods, avoiding
the ambiguities of a rectangular grid (Birch et al. 2007); (3) better visualization
et al. 2007; Burdziej 2012)
. The size of the cells have a circumscribed diameter of
300 m, equivalent to a distance of three blocks in the study areas. The size provides a
reasonable resolution at a moderate computational demand. Cells that were not
overlaying any roads were removed. The remaining hexagonal centroids were used as
origins for the analyses.
We used the location-based methods described in section 2.1. The GC OD
matrices were used to estimate the decay parameters α and β, following a
. Population-weighted centroids were used during
this process to avoid aggregation biases
(Hewko et al. 2002)
. Using ordinary least
squares, decay functions per mode of transport were fixed as: α = 1.2 and β = 0.052
for PRIV; α = 1.3 and β = 0.002 for PUB. These values were used with eq. 2 and
the density of trips attracted as BM^ to map access to jobs in both cities. The rest of
the macro-variables were analysed using the shortest travel-time to reach a facility.
All the micro-location variables were analysed using a cumulative-opportunity
method, restricted to 10 min for both mobility modes.
Then we produced integrated maps per mobility mode (PRIV and PUB), at macro
and micro level. First we standardized the results to a ‘0’ - ‘1’ scale using the non-linear
(Nyerges and Jankowski 2009)
. Then the results were
combined per mode of transport using weighted summation. The ranking by experts was
then used to estimate the normalized weights using the rank sum method
. Finally, the PRIV and PUB results were also combined using the same
procedure. The weights were equal to the percentage of trips generated per mode of transport
from each TAZ. This step made the whole analysis sensitive to types of users and the
accessibility per modality that benefits each location the most.
In the geometrical approach, we used SAA to analyse integration, choice, and their
normalised value at different spatial radii. We started with a minimum radius equal to
the average neighbourhood size (0.8 km). Then we expanded it to 1.5 km and 2.5 km.
From there we produced analyses by increasing the radius by 2.5 km up to the longest
radius that would still produce information visually different than a global metric (7.5
km in GC and 5 km in QT). We aggregated the results, originally stored per road
segment, to the hexagonal cells. Thus, each cell contains the average integration and the
maximum choice values. Finally, we produced correlation matrices comparing the
results from both approaches at p < 0.01.
Results and Discussion
Figures 3 and 4 show accessibility results per variable and per mode of transport for
both cities. In GC, job access highly benefits the core area and rapidly decreases
outwards. Access to large scale (XL) malls and grocery shops mostly benefit a corridor
area from north-west to south-east. Access to universities and hospitals tend to benefit
the most central east-western areas. Access to culture and XL sport-related facilities
(e.g. stadiums) outlines a north-south corridor. At the micro-location, low and
mediumlow access predominates in the maps. Still, the core corridor simultaneously benefits
from access to multiple locations of grocery shops, banks and restaurants, schools,
clinics and municipal markets. Access to parks points out an urban area in the
northwest. Here, various small parks are found within residential neighbourhoods.
The core area shows a relatively low-medium parks access. However, important
public and larger open spaces are located in this corridor; especially in the historic
CBD. This outlines the limitation of not including the size of each facility in the
analysis. Overall, public transport availability and travel-times strongly constrain
accessibility at macro and micro-location scale. Repeatedly, the north-eastern areas
lack good accessibility.
In QT, private access to jobs, cultural facilities and XL sports favours the most
central and historic areas. High access to XL malls and XL grocery shops reflects
an emerging commercial pole located in the north-eastern area. Highest access to
universities and hospitals are less associated with high access to other variables.
Overall, public transport restricts high accessibility to different variables, mostly
to core areas. Public transport availability and quality highly restricts accessibility.
Mostly, central and historic areas benefit from high access to various land uses.
The central area still benefits from medium high access to XL malls and grocery
shops. Also, it benefits the most from high access to all the micro-location
variables using both mobility modes.
Figures 5 and 6 show integrated macro- and micro-location accessibility maps for
PRIV (a), PUB (b) and both modes combined (c) for each city. Standardized Bs^ colour
ranges from Figs. 3 and 4 are applicable to these figures. Accessibility scores range
from 0 (red) to 100 (green). Road segments scoring high SSx NACH values, classified
in two, are overlaid on the access maps showing the combined scores (c). Discussion of
this layer is provided in next sections.
In GC the core corridor benefits the most from PRIV and PUB access given the
current mobility infrastructure to access macro-location variables (maps Ba^ and Bb^).
The combined effect of macro and micro-location accessibility, maps Bc^, is mostly
influenced by PUB accessibility. That is because the average percentage of public
transport users per TAZ is higher (0.76) than private vehicle ones (0.24). Highest macro
accessibility (scores 0.91–1) outlines three important areas in map (c): the current CBD
and two inter-connected sub-centres aligned along the CA-1. These cores match the
locations perceived by local experts as important economic centres and important job
sources. Contrastingly, current policies are mostly focused on centrality at the
northsouth core corridor. However, an opportunity to strengthen an existing polycentric
structure extends towards the north-west. Micro-location emphasises the historic centre,
core corridor, and quickly decreases towards the other sub-centres with medium scores.
Medium-low scores (0.3–0.4) point out areas that benefit from minor concentrations to
a combination of various micro variables.
In QT the modal split is equal for the whole area (50%–50%). The highest combined
macro accessibility benefits central areas and extends towards the emerging
commercial pole. Accessibility slowly decreases towards the periphery. The east periphery has
the lowest macro accessibility. Micro-location benefits core areas the most, including
the historic centre. Accessibility quickly decreases towards the periphery. Unlike GT,
the emerging pole benefits the most only from high macro accessibility.
Figure 7 shows the SSx results of integration and NACH at selected radii. In GC, the
core areas benefit the most from geometric accessibility at various spatial scales.
Neighbourhood scale integration (r0.8 km) outlines urban areas with compact grids
(small blocks). These areas correspond to some of the old planned neighbourhoods at
the core areas and the core settlements of the peripheral municipalities. These areas
have the highest potential for pedestrian movements. High integration (1700~) at r5 km
highlights the core corridor and extends towards the southern part following important
roads. Eastern, southern and western peripheral areas account for medium
integration (600–100), while some areas in between these and the core account for
lower integration values. Overall, these patterns already provide visual insights on
the important association between integration and the agglomeration of economic
and service activities in the core areas versus the peripheral areas, as outlined by
geographic access. This predominance is reflected in the disposition of city
structuring roads when observing NACH analyses (r5 km and rN). The core
corridor is the only area framed and traversed by these important roads, while
towards the periphery the urban patches are connected to this framed core by
treetype configurations. The peripheral roads play an important role structuring the
western area, producing an important intersection where one of the sub-centres is
outlined via macro accessibility.
In QT the central area is highly integrated at different spatial scales, more than the
historic centre, which is associated with the high centrality of such an area as visualized
via geographic access. High integration at rN outlines areas towards the north-west,
matching the location of the emerging commercial pole. Contrastingly, other peripheral
areas are poorly integrated at different spatial scales, denoting less consolidated
urbanization. High NACH values at the lowest radius outline service roads at
neighbourhood scale. Similarly to GC, both the central area and the historic centre
seem to be well connected by means of city structuring roads outlined by NACH
analyses (r 1.5 km and rN). Those connect to other important roads: a periphery
eastwest road on the north (intersecting with the emergent pole) and a major south-north
road on the west side.
During the analyses, integration and NAIN were both explored. However, we
observed inconsistencies of the normalized measurement at low radii, as reported in
Hillier et al. (2012)
. In GC, analyses at r0.8 km and 1.5 km were highlighting isolated
segments in peripheral areas. In QT, the same inconsistencies were observed with
analyses at r0.8 km, 1.5 km, 2.5 km and even 5 km. This was expected as the urban
layout in QT is less consolidated compared to GT.
Associations between the Geographical and the Geometrical Approach
Figures 8 and 9 show the results of Pearson correlation matrices (p < 0.01)
between geographical (location-based) and geometrical (SSx) accessibility, per
mobility mode for each city. Square size and colour range, from small to large
and from white to black, indicate correlation strength. The matrices reveal the
associations of the various accessibility measures within and between
approaches. We confirm a positive correlation between geometric and geographic
accessibility to various facilities and aggregated macro- and micro-location
accessibility. The results do not indicate a directional causality. However, high
geographic accessibility is the result of location and concentration of various
land uses. Various land uses are more prone to change or relocate over time,
compared to changes in the urban layout. Thus, we could think of the geographic
accessibility as a result of a cumulative process of land-use location influenced
by seeking optimal geometric location. We first discuss the correlations between
the various location-based analyses and then the correlations between
locationbased measurements and SSx.
Figure 8 shows that the distribution of PRIV accessibility in GC to various
macro-variables is slightly less similar compared to PUB. While PRIV mobility
infrastructure is more evenly distributed, varying locations of macro-variables
produce more irregular patterns. It reflects that some areas do not benefit
simultaneously of the same access levels to all macro-variables. In turn, the PUB
infrastructure restricts homogeneously higher access to macro-variables only to
the core areas. The remaining areas have simultaneously poorer access to multiple
macro-variables. Higher and significant correlations between macro-variables and
micro-variables in the upper matrix outline that only PRIV mobility offers
simultaneous access to macro- and micro-variables, unlike PUB.
Figure 9 shows that in QT PRIV accessibility patterns to malls, XL-grocery shops,
hospitals and universities differ highly from accessibility to various other land uses.
Partially, this is explained as the first two are located in the emerging commercial-pole.
In turn, the PUB infrastructure simultaneously benefits with high accessibility to the
same areas, a reduced portion of the city. The rest simultaneously benefits from
medium or poor access to various macro-variables. Access distributions to
microvariables are significantly similar both for PRIV and PUB. Unlike the location of
macro-variables, these are concentrated in the same areas. Similar to GC, significant
and insignificant correlations between the macro with the micro-variables show that
PRIV mobility means higher access to both groups of variables than PUB mobility.
Potential PRIV access to jobs strongly correlates with various location-based metrics
in both cities. The cumulative access to areas that attract more trips during morning
peak hours works as a latent variable showing areas favoured by good access to various
commercial and service type of land uses (job sources). As was expected, it emphasizes
the important role of such a metric to understand the distribution of economic
opportunities, as well as its potential to visualize aggregated geographic access via a robust
location-based metric using only trip data.
The highest correlations between location-based measurements (PRIV, PUB) and
NACH were found at rN in both cities. Although not all of them are significant at
p < 0.01, they are at p < 0.05. In QT correlations between PRIV location-based
accessibility to malls, XL-grocery shops and NACHrN are insignificant in both cases.
Even though correlation levels are low, these modestly provide evidence of location
preference of profitable or service land uses. It shows an association between route
choices as a function of travel time with a cognitive criteria of least angular deviation.
By looking at maps c in Figs. 5 and 6, we could suggest that NACH metrics provide
relevant information that is complementary to location-based metrics by outlining those
structuring roads from where geographic access distributes across a city. We observe
that the distribution of location-based accessibility closely follows the arrangement of
the important roads. Furthermore, this confirms the preference of the various land uses
for roads where higher flows of people are expected.
Macro variables consistently show the highest correlation with high radii integration,
rN in GC and mostly with r5 km in QT. This difference shows the effect of the different
territorial extensions and stages of urban development. In QT, high rN integration is
associated with a higher concentration of economic activities, or at least where there is
potential for those. The emergence of the west economic pole responds to this potential.
However, location-based access to most of macro-variables tends to outline more the
central areas. Therefore, high SSx integration at r5 km captures better the current location
of most of these uses. Furthermore, this also explains the disassociation between PRIV
location-based accessibility to malls and XL-grocery shops, and integration values.
In both cities micro-location accessibility shows the highest correlation with
integration at lower radii. We found that lower radii reflect restricted time-based mobility in
two ways: the 10-min threshold imposed in the analysis and mobility restrictions
imposed by the current public transport infrastructure. In GC PRIV micro-access shows
the highest correlation with integration-r5 km and PUB with r2.5 km. In QT PRIV
micro-access shows the highest correlation with integration-r2.5 km and PUB at
r1.5 km. These differences also show that micro-location land-uses in QT are spatially
distributed and more accessible to pedestrian movements, compared to GC.
The correlations provide insights about how integration at different spatial scales
(radii) is associated with access to various land uses. SSx analyses the geometric access
as an aggregated resource, assumed to be preferred by simultaneous location and
concentration of the various land uses. Aggregated location-based variables (Macro_loc
and Micro_loc) attempt to reflect this dynamic. In our case, aggregated Macro_loc and
Micro_loc address an expert-based ponderation of each land use regarding the local
land market. As expected, both aggregated variables are associated the most with
NACH values at rN in both cities, no matter the transport-mode. Correlation between
Macro_loc and integration in GC peaks at rN and at r5 km in QT for both transport
modes. The relative importance of access to jobs becomes evident in the case of QT
when addressing PUB access. In GC Micro_loc is associated most with integration at
r5 km-r7.5 km for PRIV access and r2.5 km-r5 km for PUB access. In QT Micro_loc is
associated most with integration at r2.5 km and r1.5 km correspondingly. The
divergence per mode of transport in both cities emphasizes the mobility restrictions posed
by PUB mobility using the same 10-min travel-time, but more evident for the case of
Location-based methods were adapted to consider the accessibility components as
long as data allowed it. The results are rich in information about accessibility to various
land uses: indeed, 32 maps per city. In turn, the geometrical approach considered only
two measurements (integration and choice) at different analysis radii: 12 maps per city.
SSx outputs are intuitive in terms of visualizing accessible locations, where we can
assume that profitable uses or service facilities are present. Differences in data
requirements and pre-processing give significant advantage to the geometrical approach.
However, previous training on SSx and local knowledge is required for adequate
interpretation. Besides, the applicability of the SSx might be limited in cases such as
QT, where ongoing development is sparse. Furthermore, VGI was applicable in
both approaches in this research. After exhaustive pre-processing these data became
indispensable to implement the geographic approach. In turn, data for a geometric
approach could be manually or digitally derived from other sources such as satellite
Urban accessibility was successfully mapped using a geographical and a geometrical
approach and the results were compared using correlations. We found consistent
correlations between accessibility measurements from both approaches, which allows
us to confirm our initial hypothesis. The results do not explain directionality of the
causal relation between geometrical and geographic accessibility, but it is logical to
think that in Global South cities with weak land-use policies, geometric accessibility
influences a location process where various land uses seek to benefit from reachable
locations (integration) and exposure to movement (choice).
Our research does not solve the analytical dual approach problem outlined in
. However, the results establish an empirical connection between the
morphological-geometric properties of an urban area and the distribution of
timebased geographic accessibility. The quantitative relations provide additional knowledge
on the interpretability and limitations of the information that is produced in Space
Syntax using little-data. Our results are placed in the context of studies such as
and van Nes et al. (2011), where positive associations are claimed between
SSx metrics with building density and location of various land uses, which is observed
in the present work, and is in line with Space Syntax capacity to aid in predicting flows
(Hajrasouliha and Yin 2015)
The consistency of the correlations between geographic and geometrical
measurements simultaneously validates the use of VGI, and the applicability of Space
Syntax in these two cities. Location-based analyses using VGI data produced
plausible results that outlines how access to various land uses is distributed across
each city. In turn, Space Syntax analyses applied to different urban contexts
produced information that is statistically comparable with the results of a
location-based approach. Based on the correlation results we claim that a
geometrical approach using SSx delivers plausible information from where inferences
could be made about geographic accessibility. Space Syntax turns out to be a more
data/time efficient approach.
We found some limitations when applying Space Syntax in less consolidated
urban areas, besides the problems with the normalised measurements reported in
Hillier et al. (2012)
. Global integration measurements might not reflect the
ongoing land-use processes in less-consolidated areas consistently. We cannot
conclude from the results which spatial radii best describe geographic access,
as these differed per city. This is one important drawback of SSx analysis, as
there is not enough evidence to suggest which spatial radii correlate better with
what in different cities. This observation could also apply to the MCA method
(Porta et al. 2012)
. Further research could address these observations by
replicating the research in a larger set of case studies and expanding the
comparisons by including network centrality metrics from other methods, such as the
The methodologies and results are important for the planning practice. In
Guatemala the results provide important information for transport and land-use
planning, which was not available before. Mobility and land-use projects could
be assessed using the methodologies presented here. We suggest that our
methodological framework, including the use of VGI, is replicable to other cities in
the region and Global South, where data are scarce but information is highly
relevant for planning tasks. Space Syntax would be a valid approach for
planners and researchers in areas where not even VGI is available, for
example, by analysing the attractiveness of places, the city structure, or
geometric accessibility impacts of changes in street configuration. Although
the outputs of both approaches are statistically comparable, rather than
replacing location-based methods, Space Syntax offers an important complementary
measure to geographical accessibility. We describe Space Syntax as an
accessibility tool able to support early-stage planning processes when limited data
are available. Trained interpretations enriched with local knowledge could
provide first-level insights on concentrations of profitable and public services
land use, and the distribution of access to those. Such interpretations could be
further examined in detail for areas of interest using location-based methods,
potentially supported by VGI. Finally, accessibility analysis could benefit from
addressing time, land-use and geometric aspects to improve the understanding
in other fields such as human geography and urban economy in cities in the
Acknowledgements We would like to thank the collaboration by local experts from different offices:
Guatemala City BDireccion de Mobilidad Urbana^, BSecretaría de Planificación y Programación de la
Presidencia^, BCentro Universitario de Occidente^, BInspecciones Globales^ and BFab Lab^. The work
reported in this research is funded by NUFFIC through the NICHE Project.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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