StreetExplorer: Visual Exploration of Feature-based Patterns in Urban

Street Networks

Lin Shao

, Sebastian Mittelst

adt

, Ran Goldblatt

, Itzhak Omer

, Peter Bak

and Tobias Schreck

University of Konstanz, Konstanz, Germany

Department of Geography and Human Environment, Tel Aviv University, Tel Aviv, Israel

IBM Research Lab, Haifa, Israel

Graz University of Technology, Graz, Austria

Keywords:

Street Network Visualization, Local Patterns, Urban Planning.

Abstract:

The analysis of street networks is an important problem in applications like city planning, comparison of urban

street properties, or transportation network analysis. Graph-theoretic computation schemes today provide

street network analysts with a range of topological features relating e.g., to connectivity properties of street

networks. Typically, an abundance of different network features is available, and some or all of these features

may be relevant for within- and between comparison tasks at different scales across the network. Therefore,

it is desirable to interactively explore the large segment feature space, with the goal of ﬁnding interesting

patterns based on extracted features, taking into account also the geospatial properties of a given network. We

introduce StreetExplorer, an interactive visualization system for the exploration of global and local properties

of urban street networks. The system is based on a set of appropriate similarity functions, which take into

account both topological and geometric features of a street network. Together with a set of suitable interaction

functions that allow the selection of portions of a given street network, we support the analysis and comparison

of street network properties between and across features and areas. We enhance the visual comparison of street

network patterns by a suitable color-mapping and boosting scheme to visualize both the similarity between

street network portions as well as the distribution of network features on the segment level. Together with

experts from the urban morphology analysis domain, we apply our approach to analyze and compare two urban

street networks, identifying patterns of historic development and modern planning approaches, demonstrating

the usefulness of StreetExplorer.

1 INTRODUCTION

The analysis of network-oriented data is a recurring

problem in many data analysis tasks, and to date knowl-

edge discovery and visualization has provided many

successful approaches to study network data. Many

relevant phenomena can be described by network-

oriented data structures, e.g., modeling social networks

in social science, communication networks in data in-

frastructure, or gene regulation networks in bioinfor-

matics applications.

One particular application of network-oriented data

analysis arises in the investigation of street networks in

urban areas as part of geographic data analysis. Street

networks are an integral part of any urban structure, as

they allow ﬂows of trafﬁc and pedestrians to connect

and commute between different parts of the city. In

conjunction with respective features within a city e.g.,

land usage or trafﬁc, street networks may determine

important functional, social and perceptual properties

of urban settlements, such as the efﬁciency of trans-

portation and space utilization, social residential seg-

regation and wayﬁnding. Recently, graph-theoretic

methods have become popular to study topological

properties of street networks (see Section 2.1). In prac-

tice, topological measures (also called features) like

connectivity, integration or axiality can be computed

for each street segment of a larger street network. Each

of these features are typically given as real numbers,

indicating e.g., how central a street segment is to the

whole network. Experts are interested in investigating

the properties of street networks, by inspecting the dis-

tribution of the different features across the street net-

work, and correlating them with geometric and other

Shao, L., Mittelstädt, S., Goldblatt, R., Omer, I., Bak, P. and Schreck, T.

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks.

DOI: 10.5220/0005771800840095

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 2: IVAPP, pages 86-97

ISBN: 978-989-758-175-5

properties of the street segments. However, the analy-

sis and comparison of local patterns based on different

street network’s conﬁgurational attributes is a common

problem to city planners and geographers. The sim-

ilarity of these local patterns may depend on several

criteria, e.g., extracted features, geometric properties

or provenience, and the identiﬁcation of outstanding

pattern classes can help to enhance urban designs, city

planning, transit-oriented development, or to study his-

toric developments. For this purpose, it is useful to

investigate meaningful patterns within and between

cities, and apply the conclusions drawn from the anal-

ysis to future urban development.

We have worked with experts from urban morphol-

ogy on the problem of analyzing topological and geo-

metric features occurring in street networks. We iden-

tiﬁed the need for exploratory approaches to cope with

the analysis problem, due to several factors, which

make rule-based or purely automatic data analysis not

fully effective. First, computational graph analysis

provides a multitude of different topological features.

Which of the feature is important for the current analy-

sis is, however, not known a priori. Then, by consid-

eration of geometric features, relevant patterns may

occur at different scales with respect to the network,

e.g., a smaller or larger local area may be of interest

for comparison. Both calls for a highly interactive,

exploratory approach to investigate street network fea-

tures since it is not known a priori, which features are

of interest, and at which scales.

We contribute an application for highly interactive

visual exploration and comparison of local patterns

across a network. We apply interactive search and

appropriate visualization for local patterns of street

features for a domain expert to investigate his data col-

lection. At the heart of our approach is a ﬂexible search

function, by which the expert can quickly indicate for a

speciﬁc network feature the regions of interest. We de-

ﬁne a suitable similarity function to rank and compare

street network properties, taking into account topolog-

ical features, but also spatial properties of the network.

We introduce a suitable color-mapping and boosting

scheme, which allows visualizing local similarity to

a user query in context of the overall feature distri-

bution. It allows the user to quickly verify different

hypothesis regarding to recurrent patterns, and arrive

at meaningful ﬁndings on a given street network.

The remainder of this paper is structured as fol-

lows. In Section 2, we recall related work on analysis

of urban street networks, pattern analysis in graph data,

and on visualization of spatial data. In Section 3, we

introduce the basic idea of our approach, based on two

modes of query speciﬁcation and result visualization.

Then, in Section 4 we describe in detail our search

methods and the similarity function behind the search.

Further, in Section 5, we demonstrate the effectiveness

of StreetExplorer by a use case application on a real

street network analysis, conducted together with our

co-author domain experts. We also provide a discus-

sion of advantages and limitations of our approach.

Finally, Section 6 concludes.

2 RELATED WORK

We discuss related work in street network analysis,

pattern extraction and visualization, and spatial visual-

ization.

2.1 Analysis of Urban Street Networks

Street network patterns are a dominant component of

a city’s spatial properties. They have been shown to be

signiﬁcant for human spatial behavior, such as trans-

portation mobility and accessibility (Marshall, 2004)

and vitality of urban life (Wheeler, 2008). Urban street

networks have been investigated with respect to their

geometric and conﬁgurational attributes (e.g., number

of intersections, number and size of blocks, connec-

tivity, integration, fragmentation, etc.), their dynam-

ics as well as their relations with other morphologi-

cal components such as buildings, lots and the like.

Space syntax is currently the dominant theoretical and

methodological approach, which is based on conﬁg-

urational attributes of urban street networks (Hillier,

2002; Hillier, 2007). This approach concentrates on

the integration between urban streets (or places) and

their relative accessibility and centrality in terms of

intermediacy (Kropf, 2009). These conﬁgurational as-

pects have been found to be reliable indicators for pur-

poses of comparison between street patterns (Hillier,

2002; Vaughan et al., 2010) and for distinguishing

street pattern development, e.g., self-organized ver-

sus planned-cities ((Porta et al., 2006; Jiang, 2007)).

This body of research has produced classiﬁcations of

these patterns according to the spatial conﬁguration

attributes of street network.

The space syntax attributes, which are based on

axial maps, i.e., the smallest set of direct axial lines

map of each investigated city (Hillier, 2002; Hillier,

2007), represent several aspects of accessibility and

centrality at different scales that can be used for a

classiﬁcation of street networks. However, due to the

multidimensional character of street networks’ spa-

tial conﬁguration, the identiﬁcation and classiﬁcation

of street patterns in urban areas is not a simple task.

Space syntax studies have shown that street patterns

can be identiﬁed by measuring axial lines or segments

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks

through a presentation of geographic distribution of

spatial conﬁguration attributes at different geographic

scales. Such presentation can support the deﬁnition

of spatial pattern patches in the street network at dif-

ferent scale in terms of their internal structure, con-

textual structure and relations between the two (Yang

and Hillier, 2007). Such task requires interactive and

complex actions by experts in the ﬁeld of urban mor-

phology.

2.2 Pattern Analysis in Graph Data

In graph and network analysis, similar to street net-

work analysis, experts are interested in recognizing

local patterns or subgraphs containing meaningful and

relevant information. Since both research areas are

highly connected they also share similar approaches

to address related challenges. (Yang et al., 2014; Tian

et al., 2012) use graph based approaches to identify

signiﬁcant patterns in street networks. Street networks

were represented as graphs, whereas streets are con-

sidered as edges and road junction as nodes. A large

body of previous work addresses the visual analysis of

graphs by search-based pattern exploration, of which

we here can only give a small, illustrative sample. In

(von Landesberger et al., 2009) a visual analysis sys-

tem was introduced, which automatically identiﬁes

common graph patterns (motifs) that are used as basis

for navigation and exploration in graph data. In (Yan

et al., 2006), a graph-substructure similarity search

based on graph features was discussed. One key as-

pect of graph analysis is the representation of patterns

in a suitable manner. (Dunne and Shneiderman, 2013)

introduced a motif simpliﬁcation technique, in which

common patterns are replaced by meaningful glyphs.

More generally, a survey of methods for visual anal-

ysis of graphs is given by (von Landesberger et al.,

2011).

2.3 Visualizations of Spatial and

Movement Data

Many useful visualizations to date support interactive

analysis of geospatial and movement data, of which

we again can only give a small overview here. An

encompassing overview of visual analytics approaches

and tools for movement data is covered by (Andrienko

et al., 2013). (Bak et al., 2010) introduced an ap-

proach for joint visual analysis of urban land usage

and street network properties based on visual cluster

analysis. In (Chu et al., 2014; Ferreira et al., 2013)

mobility patterns of taxi movements were investigated

by extracting geographical information and using vi-

sualization techniques. Recent work in street network

analysis (Wang et al., 2013) uses taxi GPS data to com-

pute trafﬁc ﬂow rates and estimate trafﬁc jams in the

city. A visual analysis tool for support of urban space

and place decision-making processes was developed in

(Pettit et al., 2012), relying on visualization techniques

including time cubes, heat maps, and choropleth maps.

We build on these works for visualization of net-

work and spatial data, contributing an approach for the

explorative analysis of street network features based on

adaptive selection-based search over geographic and

topological properties of the street network, including

appropriate data visualization.

3 OVERVIEW OF OUR

APPROACH

The aim of StreetExplorer

is to support street network

analysts during their investigation by supporting inter-

active search for similarities within the network, giving

rise to potentially interesting local patterns. We rely on

a wealth of currently existing computational methods

for topological street network features, which we help

to explore and understand by means of a search-based

visual analysis system.

The exploration design of StreetExplorer is based

on a two-step comparison approach that enables an

investigation of street patterns on the global and local

scale within and between urban networks. The design

is depicted in Figure 2. We adhere to Shneiderman’s

Information Visualization Mantra - Overview First,

Zoom and Filter, Details on Demand (Shneiderman,

1996) and propose to start the analysis session with

a global overview comparison of features in a given

network (shown in Figure 1). By means of our small

multiple view, analysts may recognize feature similari-

ties, dependencies or correlations that help to ﬁnd an

interesting feature for further investigation.

We start the exploration by applying a global com-

parison of all available network features, to ﬁnd a start-

ing point for the exploration, and to identify interesting

local patterns from a large number of pre-computed

features. All available features are given by a list view

to select from in the left hand side of Figure 1. This

view displays all features in a hierarchical structure

based on the street-networks’ conﬁgurational attributes

and resolution levels. Thus, features containing a high

number of resolutions will be aggregated in the list and

provide a better overview than a plain list. From this

list, several features may be chosen and visualized in a

small multiple view that represents the feature values

A demonstration of StreetExplorer can be found at:

https://vimeo.com/149003539

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Figure 1: Feature exploration in StreetExplorer to compare

space syntax conﬁgurational attributes of two cities, namely

Kfar Saba and Ashdod. The system creates a small multiple

view of all selected features for comparison and further in-

depth investigation for similarity in the street-network.

by their original proportion of the city’s street network.

This allows for global comparison of the features. To

support the global comparison, the features (or maps)

can also be sorted according to the similarity of a se-

lected one, by means of the below discussed similarity

function (Section 4).

As a next step, the analyst could apply zooming

and panning interactions to explore the features for

interesting local patterns. To ﬁnd locally interesting

patterns, we support the query formulation by two

selection-based methods, which are depicted on the

left of Figure 2. The deﬁnition of query patterns can

also directly be performed on the map by selecting cer-

tain street segments. Thereby, the analyst may deﬁne

patterns of interest, which are composed of geomet-

rical, topological and connectivity information of the

individually chosen segments. The similarity search

Figure 2: Our proposed procedure to analyze street network

patterns. The exploration starts with an interactive compar-

ison of local patterns for a selected feature. This can be

done by a district search or a user-deﬁned search based on

the segment level. Matching results will be highlighted on

the map, and thus provide an overview of the availability,

frequency and distribution of feature-based patterns. After

interesting patterns were found, the analysis can be expanded

to compare among several cities.

occurs interactively and the best matching results will

be directly highlighted on the map.

Finally, after some interesting local patterns have

been identiﬁed for a given street network, the analysis

may continue by comparing the found pattern across

different features and possibly, also across networks

of different cities (depicted on the right of Figure 2).

4 EXPLORATION OF STREET

NETWORK PATTERNS

We describe the similarity functions of StreetExplorer

as an important part of our supported exploration ap-

proach outlined in Figure 2. Further, we discuss novel

colormapping techniques to enhance the readability of

topological features in street networks and to support

interactive highlighting.

4.1 Comparison of District Patterns

StreetExplorer enables the search and comparison of

feature similarities on the global and local scale within

and between street networks. In case these proper-

ties cannot be seen at ﬁrst sight, the interactive search

methods of StreetExplorer can be utilized to explore

the street network for locally interesting properties.

We support the small multiple view by a concurrent

search, meaning that a given query is run against all

maps currently shown in the small multiple view, and

matches are highlighted on all features maps at the

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks

same time. To this end, we provide two local search

functions for street network patterns. The ﬁrst search

function is a district comparison, which identiﬁes sim-

ilar regions based on user-deﬁned districts. Analysts

may deﬁne a rectangular region by using a rubber band

selection and detect similar districts based on geomet-

ric properties and feature values of the particular street

segments. This is particularly suited if analysts want

to approximately compare an area of interest e.g., east

or west peripheral area of a city. The technique we

use to compute similarity between districts in a street

network is adapted from image retrieval (Liu et al.,

2007) and relies on the distribution of feature values

as a basis for the similarity function.

In our approach, we use an equal-width histogram

of all street segment features within the given selection.

The histogram is normalized according to our color-

mapping scheme (see Figure 3 and Section 4.3). The

bin size conﬁguration is an ill-deﬁned problem, which

depends on the data set. This conﬁguration can be

adjusted by the analyst himself, but to achieve similar

areas in accordance with the visual perception of the

color-mapping, we suggest a minimum bin size that

contains a stable color interval and covers all feature

values that are visually in the same range. We ﬁgured

out that the good results were achieved by using a

histogram with 10 bins (one bin for each color) that

contain further 10 interpolated hues, as demonstrated

in Figure 3. Consequently, the histogram comprises

in total a range of 100 units in the normalized feature

interval between

[0, 1]

. We then store the length of

all segments lying in the query district to the corre-

sponding histogram bins and iterate the search over

the entire map using a sliding window approach to

ﬁnd the most similar regions. The sliding window

approach is a method that sequentially compares lo-

cal regions for similarity search. In this approach, a

street network is divided into a two-dimensional grid

and the similarity search is performed in each window.

Consequently, the histogram will take all intersecting

segments into account and is weighted by the length

of street segments. To assess the district similarity,

we divide the street network into a uniform-sized grid

and translate the rectangular district box over the grid

including overlapping areas. For each iteration, a new

histogram is computed and compared with the query

district by the Euclidean distance. Due to the complex

problem of scaling, the grid size can be interactively

adjusted by the user, but at this point we would like

to point out that a too small grid could cause many

matching results at the same region (slightly shifted)

and a too coarse grid could lead to missing associa-

tions. By default, the grid size will automatically be

determined according to the query size, e.g., a smaller

Figure 3: Illustration of our histogram approach to compute

district similarity. The histogram contains 10 bins that cover

further 10 interpolated hues according to a spiral colormap.

Street networks are partitioned into a regular grid and a

sliding window approach iterates the search through the

grid.

query district will also produce a smaller grid size.

The grid size corresponds to a quarter of the original

query size, and thus enables an overlapping degree of

a half width and length. In the end, the regions with

the most similar histograms will be emphasized on the

map, as shown in Figure 2 (District Pattern).

4.2 Comparison of Segment Patterns

To ﬁnd more ﬁne grained street patterns the second

local search function of StreetExplorer can be used,

which is shown in the lower left corner of Figure 2

(segment pattern). It takes the connectivity as well as

the length, direction and feature value of individual

street segments into account. To distinguish the main

segment orientations our domain experts deﬁned 8

types of directions as shown in Figure 4 (a). Segments

are considered as similar if they possess approximately

equal direction, and are within a tolerance margin of

length and feature value. This ensures that similar

street patterns also consist of approximately the same

segments irrespective of connection. Furthermore, we

propose two different selection-based approaches to

specify the neighborhood of a given segment and form

a pattern.

The ﬁrst selection is deﬁned by a connected k-hop

clustering, which uses a selected segment as cluster-

head and groups several neighbors as members based

on the hop distance. For instance, a 1-hop street pattern

consists of one segment (clusterhead) and connected

segments, in which the distance between the cluster-

head and its members is 1 hop (junction). For better

performance, we pre-calculated all segment connec-

tions in advance. An illustration of our k-hop selection

is shown in Figure 4 (b). The orange marked segment

denotes the selected segment, whereas the blue and

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

(a) Direction types. (b) Illustration of k-hop.

Figure 4: Illustration of our similarity properties for search-

ing k-hop based patterns. (a) shows the eight different di-

rection types; (b) illustrates the members of a street pattern

based on 2-hops (blue and green segments).

green ones are the members that are reachable by a

path of one and two hops respectively. By means of

this selection approach a street pattern can easily be en-

larged by increasing the number of k (shown in Figure

7 (a) - (c)). In this approach, the selection of the ﬁrst

segment is crucial since it builds the basis of the street

patterns and initializes the similarity search based on

its neighborhood. After a street segment has been

deﬁned as core segment (clusterhead), a single seg-

ment search over all target segments takes place, and

computes an overall similarity score for its connected

neighbors. Consequently, the analyst can quickly in-

vestigate similar local areas of a street network by

changing the clusterhead segment. Since street pat-

terns may have different neighborhood sizes, we com-

pute a similarity score by comparing the average of

feature value and the segment length of all segments

that belong to one street pattern. Equation 1 shows

the computation of the weighted average feature value

where

is the number of neighboring segments of one

street pattern. Accordingly, we determine the distance

of the query to the target street patterns

(Feature

avg

)

and eliminate those patterns, which exceed a certain

threshold. Analysts may determine k for the neighbor-

hood size as well as the tolerance margin for segment

length and feature value to steer the analysis process.

Feature

avg

∑

i=1

SegmentLength

× FeatureValue

∑

i=1

SegmentLength

(1)

Alternatively, the analyst can also switch to a free-

form selection in order to deﬁne an interesting pattern.

By means of this selection approach, the analysts are

able to specify even more accurate street patterns by

selecting individual segments. In this way, it is possi-

ble to form complex street patterns that contain, e.g.,

stringy, circular or chain-like structures. The basic

similarity search step is applied for each added seg-

ment and a connectivity comparison veriﬁes whether

the matched segments are connected correctly or not.

Beginning from the ﬁrst segment, the connectivity

comparison stores the connection of each new added

segment and observes the connectivity of query and

Figure 5: Demonstration of our segment connectivity com-

parison for searching individual segment patterns. The color

coding indicates the order of selected / matched segments.

Based on the ancestor connection property, the search is

invariant against pattern transformations.

target patterns in real time. Figure 5 demonstrates an

example with four correct and one incorrect connected

patterns referring to the query instance. After each new

added segment, query and target patterns are compared

for structural similarity of their neighboring segments.

This means that every potentially similar segment is

required to have an identical connected ancestors, oth-

erwise it will be eliminated from the result set. For

instance, all four correct target patterns in 5 have an

equal connection to their ancestor segments (segment

1 is connected to segment 2 and segment 2 is again

connected to segment 3), and thus are considered as

similar. The problem with the negative example is

that the last added segment (segment 3) is connected

to both ancestor segments (segment 1 and segment

2), and are thus considered as dissimilar. To this end,

our approach is invariant to rotation and allows slight

variations of target patterns.

These local search functions can also be applied

for intercity comparison. Basically, the search tech-

niques and interaction possibilities for this application

are the same as for intracity comparison. The only dif-

ference is that additional street networks are displayed

in separate small multiple views and can include in-

dividual features (see Figure 1). In this case, it might

be beneﬁcial to compare local patterns in different

street networks (cities / features), which the analysts

have considered as interesting. Hence, we designed a

portable pattern search that can easily transfer street

patterns (queries) of one city to another city. Accord-

ingly, it enables additional comparison tasks and can

reveal interesting ﬁndings, e.g., searching for certain

patterns in all street networks; or comparison for spa-

tially similar located patterns (northern part of the city

or downtown); or searching for similar kinds of roads

(main streets).

4.3 Visual Boosting

The visualization and selection of segments can be

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks

(a) Without any boosting schemes. (b) With visual attention boosting. (c) With color contrast boosting.

Figure 6: Demonstration of our visual boosting approach on the street network of Kfar Saba. To enhance visual perception of

local patterns, we used a spiral colormap to show the feature values of street segments (a); reduced the saliency of unselected

segments (b) and adapted the border color of located street patterns (c).

perceptually supported by novel colormapping and

visual boosting approaches that we discuss in the fol-

lowing.

Colormapping.

(Mittelst

adt et al., 2015) provide

state-of-the-art guidelines and the tool ColorCAT to

design colormaps for combined analysis tasks. In our

application, the color encoded features are continuous

and their interpretation requires the identiﬁcation and

comparison of metric quantities. We use ColorCAT to

design our color encoding (see Figure 3) for our ap-

plication. The number of distinct colors is maximized

for accurate identiﬁcation of color values (Ware, 1988;

Kindlmann et al., 2002;

) and the perceptual linearity

is preserved by linear increasing intensity, which is

required for comparing color encoded data.

Boosting with Color Contrasts.

If many streets are

sharing dense areas of the display, it is hard for the

user to perceive single streets. Studies showed that the

visibility of low contrasts is reduced in high spatial

frequency areas of the display (Barten, 1999). The

method of Mittelst

adt et al. (Mittelst

adt et al., 2014)

compensates for harmful contrast effects in order to

accurately visualize color encoded data. The method

applies a perception model (Fairchild and Johnson,

2004) in order to estimate the bias of contrast effects,

which are ampliﬁed in the cones of our eye. With this

approach it is possible to approximate a color

that

is in maximum color contrast to a target color

with

= D65

− c

(with

being the LMS channel in the

CAT02 color space and

D65

is the standardized refer-

ence light). Note, that this is only valid for saturated

colors with one of the channels being close to zero. In

order to visually boost the readability of streets in our

visualization, we draw borders around the segments of

streets. These borders are encolored with maximum

color contrast to the segment color, which accords

to boosting with color (Oelke et al., 2011). Further,

we enable the user to control the segment and border

sizes which enhances readability. On the one hand,

these contrast effects can bias the user in reading color

encoded features if the task is focused on a detailed

analysis of speciﬁc (zoomed-in) data objects. On the

other hand, this approach enhances the perception and

recognition of streets in overviews, which enables us

to read features even for dense areas on the screen.

Therefore, we recommend the approach for tasks that

require overviews of high frequency data. Further, this

approach can be applied to highlight segments and

street patterns (see Figure 7).

Boosting with Visual Attention.

The most common

reason to highlight visual elements with color is to

attract visual attention. Studies of (Camg

oz et al.,

2004) show that humans are predominantly attracted

by bright and saturated colors. Since we use both

to visualize metric quantities and want to accurately

encode the elements in the selection, we reduce the

visual saliency for unselected elements. We argue

that these segments are still important for the context

information of the selection but they need not to be

visualized as prominently as the selected elements.

Therefore, we reduce the intensity and saturation of

these segments (we set both to 50% of the original

color in the HSI color space (Keim, 2000)). To further

decrease the visual saliency, we use the borders of

unselected elements and decrease the contrast to the

segment as well to the background by selecting the

same color for the border but adjust the lightness and

saturation of the border color to

50%

of the original

color. The low brightness and color contrasts reduce

the visual saliency of unselected elements and steer

our attention towards the selection.

Visual Boosting Effect.

Figure 6 demonstrates the

effect of our visual boosting approach. First, a spi-

ral colormap is applied to visualize the continuous

feature values of each street segment in the entire

city (a). The colormap ranges from green/blue (low

values), through purple/red to orange/yellow (high val-

ues). Consequently, analysts can easily detect streets

with high feature values or areas including low feature

values. Second, we reduced the visual saliency of in-

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

considerable street segments and thus, highlight local

patterns resulting from similarity search (b). However,

one remaining challenge is to perceive the patterns and

their feature values, in particular in the case of rapid

changes during the exploration. To tackle this issue,

we ﬁnally support the highlighting by increasing the

segment size and adapting the border color of detected

local patterns (c). By default, the segment sizes are

adjusted to the global street network view (without

zoom level). We suggest to increase the segment size

when focusing the analysis on a local area (with higher

zoom level) to maintain the colors and feature values

of the particular street patterns.

We designed two search functions to investigate lo-

cal similarities in street network features. Analysts are

able to deﬁne local patterns based on geometrical fea-

tures (length, direction and composition) and analyze

the characteristics on topological features (angularity,

axiality or connectivity), which is applied in Section 5.

Feature values will be presented by our color-mapping

and help together with our boosting scheme to reveal

the similarity of discovered patterns. The search can

be used within one chosen feature to detect similar

street patterns, or over several features to identify the

distributions of patterns based on different topological

features. An analysis without network connectivity

constraints is also possible and can be achieved by

using the district search. Another possibility would be

to analyze the pattern without the inﬂuence of topolog-

ical features, which corresponds to a basic subgraph

search in network data.

5 CASE STUDY

In the previous section, we introduced the StreetEx-

plorer system for search-based exploration of feature

properties of urban street networks. We have worked

closely with urban modeling experts from a university

to use our system, and will describe the ﬁndings next.

The expertise of the users reaches back two decades

in research and consulting for urban planing and mod-

eling, progressing the state-of-the-art in the domain

signiﬁcantly. Relevant application and research ﬁelds

of the experts include deﬁning effect of land-use types

on ethnic residential integration, commercialization

developments of urban neighborhoods, and predicting

street segments’ pedestrian friendliness and walkabil-

ity. Understanding and analyzing the topology and

conﬁgurational attributes of street networks are indis-

pensable to these research and application domains.

5.1 Features Exploration

The network data, together with topological features

from street-networks’ conﬁgurational attributes has

been provided to us in standardized shape ﬁles. The

investigation was carried out by the experts and as-

sisted by the developers. Measurement of space syntax

conﬁgurational attributes is based on topological anal-

ysis of axial maps that treats individual axial lines (see

Section 2.1) as nodes and axial line intersections as

edges of a connectivity graph. The resulting graph

provides the basis for several space syntax measures

that describe the centrality of individual axial lines.

In the current study, we have used the following mea-

sures: Connectivity, Local Integration, Global Inte-

gration, Local Choice and Global Choice. For any

particular axial line, Connectivity denotes the number

of directly linked axial lines. Global Integration indi-

cates the closeness of an axial line to all other axial

lines in the system by computing the shortest distance

(or step depth) of the respective line from every axial

line in the entire urban area. The Local Integration

limits this computation to a certain neighborhood size,

which is limited by a deﬁned number of directional

changes. Against to the above to-movement measures

that represent the accessibility of a given axial line, the

measures of Global Choice (which is equivalent to the

graph-based centrality measures of betweenness) and

Local Choice are through-movement measures. These

measures indicate the number of times a location is

encountered on a path from origin to destination for

all pairs of axial lines in the entire urban area (global

choice) or up to a deﬁned topological distance (local

choice).

In this study we chose to investigate two cities -

Kfar Saba and Ashdod - which are representative of

the street patterns of Israeli urban space (Omer and

Zafrir-Reuven, 2010). We started our investigation

by selecting the ﬁve space syntax measures, as de-

scribed before, in order to reveal the global and local

similarity between these cities. Results for the two

cities are shown in a small multiple view (Figure 1) for

comparison. This representation clearly reﬂects the

conﬁgurational differences between the cities, which

has historical, developmental and topological reasons.

Kfar Saba is a city with a nearly orthogonal street pat-

tern that was established in the beginning of the 20th

century and has developed mostly according to the

pre-modern planning approach. In contrast, Ashdod

is a relatively new city that was established in 1957

according to a comprehensive city plan based on mod-

ern planning approach. The city is characterized by

a tree-like street layout associated with the idea of

neighborhood units. These differences between these

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks

planning approaches and the associated street patterns

are similar to other western cities (e.g. (Marshall,

2004)).

Data on the cities’ street networks were obtained

as GIS layers for the year 2012 from MAPA company

(http://www.gisrael.co.il). The Depthmap software

((Turner, 2001)) was used for constructing and analyz-

ing the axial and segment maps and for computation

of space syntax measures.

5.2 Interactive Search and Findings

We started our investigation with comparing similar

local areas by using our district comparison tool. We

realized very quickly that the distribution of similar

local areas vary at most during the exploration of the

feature Local Integration (second row of Figure 1) and

decided to use this feature for further investigation.

Moreover, previous research indicates that this mea-

sure best distinguished cities from each other (Omer

and Zafrir-Reuven, 2010) and represent most effec-

tively the differences in street patterns. Local integra-

tion measure describes integration only up to a deﬁned

number of changes of direction (topological distance),

which is usually equal to three. The use of segments

(the lines between junctions of axial lines) enables net-

work analysis on a ﬁner scale than using axial lines, it

also extends with a consideration of angular distance

(least angle distance) and metric distance that might

be relevant to the purpose of investigation.

Our StreetExplorer implementation provided rapid

response to a series of interactive queries we con-

ducted, iteratively switching between selections of

different segments in the overall street network, to ﬁnd

interesting repetitive structures in the map. We have

been mainly focusing on areas that were established ac-

cording to the modern planning approach. After a few

trials, we selected one segment in the peripheral area

of Kfar Saba (Figure 7) and in Ashdod (Figure 8). As a

result, StreetExplorer highlighted all similar segments

in the entire street-network for further investigation.

Similar segments were found only on new areas that

were developed in the second half of the 20th century:

in the west and east peripheral areas of Kfar Saba,

and almost in the entire area of Ashdod. We then in-

creased the neighborhood level in a step-wise manner

from no neighborhood (single segments are compared)

to second and forth degrees of neighborhood (k-hop)

included in the similarity function.

As shown in Figures 7 and 8, the geographic dis-

tributions of similar patterns in both cities remained

stable and increased consistently across the neighbor-

hood level. Even though, when neighborhood size

increases, some of the smaller patters that were close

by, merge to larger ones, but some of them drop out, as

their similarity falls below the deﬁned threshold. This

means, that by selecting the neighborhood size, the

user in StreetExplorer can interactively determine the

size of the pattern of interest and get immediate visual

feedback on the search results.

In the latter query modality, the users selects only a

single query segment, and the system increases the

neighborhood size resolution along connected seg-

ments, thereby providing several query alternatives

with different size. We note that the geometric simi-

larity of the matches to the queries (perceived mainly

by segment length and direction) is more prominent

when the neighborhood size used in the similarity com-

putation is comparably low. From a domain analysis

perspective this makes sense, as geometry and form

are mainly considered more local conﬁgurations of

attributes, and rather not applicable to district and city

levels.

Overall, this quite accurate pattern retrieval can

be related to the consideration of topological spatial

integration level at different network scales as well

as to the geometric properties of segment length and

direction. The revealed similar patterns are character-

ized by the tree-like structure of the street network. In

addition, distances between intersections on arterials

are relatively larger than the distances in traditional

patterns, and only streets at the same hierarchy or one

above or below it, can intersect with the arterials. All

these attributes do not exist in the traditional street

patterns located in the older areas in the center of Kfar

Saba.

Despite the similarity between street networks that

are established at the same period in both cities regard-

ing the location typical street patterns, they differ in the

level of homogeneity within the street patterns. In Kfar

Saba the neighborhoods are relatively homogeneous

regarding their Local Integration value over all resolu-

tions while in Ashdod the neighborhoods get quickly

very heterogeneous when increasing the spatial resolu-

tion. This is visually salient in the number of distinct

colors the segments have within a highlighted con-

ﬁguration. Moreover, while in Kfar Saba the similar

neighborhoods remain in the periphery, independently

of the resolution level, in Ashdod the neighborhoods

spread to all districts of the city and quickly cover al-

most all the residential districts. The heterogeneity of

the street patterns in Ashdod is a result of the hierar-

chical structure of the neighborhoods’ street networks,

which is reﬂected well in the spatial patterns of Lo-

cal Integration, where arterial roads with the higher

integration levels separate neighborhoods from each

other. Against that, the hierarchy of importance in

Kfar Saba is much weaker with no clear distinction

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

(a) No neighborhood selected.

(b) 2-hop neighborhood.

Figure 7: Kfar Saba use case shows the distribution of similar

patterns in the city’s peripheral area at different scales. A

typical conﬁguration is highlighted at the bottom left.

between neighborhoods.

Thus, unlike previous space syntax works that were

based on the spatial patterns of space syntax attribute

values of individuals segment or axial lines (i.e. neigh-

borhood at level 0), the investigation on pattern simi-

larity in street networks here is conducted at different

neighborhoods size and with simultaneous considera-

tion of topological spatial integration and geometric

aspects of street network (i.e. length and direction of

segments). Due to these capabilities of this ﬂexible

search mechanism in StreetExplorer, we have identi-

ﬁed street patterns that characterize urban planning

approaches as well as sub-types street patterns, in this

application, among modern street-based planning.

(a) No neighborhood selected.

(b) 2-hop neighborhood.

Figure 8: Ashdod use case shows the distribution of simi-

lar segments in the city’s peripheral area. Geometric form,

length and direction, are only marginally included in the

similarity computation and only in the lowest neighborhood

resolution.

5.3 Expert Opinion

The unique contribution of StreetExplorer to the pre-

sentation and analysis of urban street network is

twofold as stated by our co-author domain experts. At

ﬁrst, the simultaneous consideration of topological-

visual (space syntax attributes) and geometric (i.e.

length and direction of segments in Euclidean terms)

aspects of spatial integration pattern in urban street

network are innovative. Both aspects are essential

for describing and identifying patterns in urban street

networks (Marshall, 2004) and should be considered

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks

together in street network analysis. For instance, in

(Marshall, 2008) topological features, such as continu-

ity, connectivity and depth, of local street patterns were

extracted and compared with a set of pre-deﬁned pat-

terns on a triangular projection space. StreetExplorer

enables this simultaneous assessment and allows to

represent user-deﬁned patterns in their original spatial

position. Secondly, for the analysis it is indispens-

able to represent similar patterns at different neighbor-

hood sizes beyond the usual presentation at the level

of individual segments. Consequently, the ability to

identify street pattern pieces in urban street network

as previously described enables the building of a ty-

pology of street patterns according to spatial cultures

or urban planning approaches. This leads to explo-

ration of street patterns for the emergence and design

of urban phenomena such as walkable environment,

retail activity and urban center, legible environments

(wayﬁnding), residential segregation and crime. In ad-

dition to the current use of spatial integration values at

the level of individual segments or axial lines in urban

models (e.g. pedestrian or vehicle volume models) one

could incorporate spatial integration values at the level

of street pattern pieces that are deﬁned by different

neighborhood sizes. Finally, the experts stated, that

they are not aware of any alternative tool or method

that provides such interactive analytic functionality.

6 SUMMARY AND FUTURE

WORK

We introduced StreetExplorer that helps analysts to

explore a given space of network features in their con-

text with a set of available topological street features

provided by domain-speciﬁc software. The goal is to

speciﬁcally support the exploration and visual com-

parison of street features on the global and local scale

across and between features and regions in an effec-

tive way. We supported an interesting new application

domain for visual analysis, and we provided three con-

tributions in our work as follows. First, we deﬁned a

suitable similarity function to rank and compare street

network properties, taking into account topological

features, but also spatial properties of the network. Sec-

ond, we deﬁned suitable interaction functions, which

allow the user to interactively select local areas of in-

terest based on free-form selection and an adaptive

neighborhood deﬁnition. Third, we deﬁned a suitable

color-mapping and boosting scheme, which allow the

visualization of local similarity to a user query in con-

text of the overall feature distribution. Additionally,

we applied StreetExplorer together with domain ex-

perts, demonstrating the effectiveness and usefulness

of the chosen designs by showing unexpected ﬁnd-

ings. While in the past the analysis has focused on pre-

deﬁned patterns like grid, star, or ring road patterns,

now analysts are able to generate accurate patterns ac-

cording to their interests. By means of StreetExplorer

the domain experts were able to ﬁnd the most infor-

mative feature and discovered interesting distributions

of local patterns that can be traced back to historic

development and modern planning of urban networks.

In the future, we plan to extend this work in dif-

ferent directions. We will enrich StreetExplorer on

the metadata level by including additional features,

such as landuse, barrier and general user comments,

which can be extracted from openstreetmaps and pro-

vide an extensive search for local patterns in street

networks. Moreover, StreetExplorer can be extended

by different clustering methods for supporting com-

parison of interesting patterns. For instance, details-

on-demand functions could be used to show the most

signiﬁcant or frequent patterns and propose further

information for analysis. We want to investigate ana-

lytical methods to detect suitable parameter settings

that reveal interesting patterns. This could be realized

by using image-based techniques and comparison al-

gorithms that consider several outcomes of parameter

variations. We also would like to introduce functionali-

ties for comparison of topological features and provide

a semi-automatic feature selection approach based on

statistical measurements. Furthermore, the visual rep-

resentation and global arrangement of maps can be

enhanced by specialized layouts that take the spatial

properties of features into account.

ACKNOWLEDGEMENTS

This work was partially funded by the Juniorprofessor

Program of the Landesstiftung Baden-W

urttemberg

within the research project Visual Search and Analysis

Methods for Time-Oriented Annotated Data.

REFERENCES

Andrienko, G., Andrienko, N., Bak, P., Keim, D., and Wro-

bel, S. (2013). Visual analytics of movement. Springer

Publishing Company, Incorporated.

Bak, P., Omer, I., and Schreck, T. (2010). Visual analytics of

urban environments using high-resolution geographic

data. In Geospatial Thinking, Lecture Notes in Geoin-

formation and Cartography, pages 25–42. Springer.

Barten, P. G. (1999). Contrast sensitivity of the human eye

and its effects on image quality, volume 72. SPIE press.

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Camg

oz, N., Yener, C., and G

uven

c¸

, D. (2004). Effects

of hue, saturation, and brightness: Part 2: Attention.

Color Research & Application, 29(1):20–28.

Chu, D., Sheets, D., Zhao, Y., Wu, Y., Yang, J., Zheng, M.,

and Chen, G. (2014). Visualizing hidden themes of

taxi movement with semantic transformation. In Pa-

ciﬁc Visualization Symposium (PaciﬁcVis), 2014 IEEE,

pages 137–144.

Dunne, C. and Shneiderman, B. (2013). Motif simpliﬁca-

tion: Improving network visualization readability with

fan, connector, and clique glyphs. In Proc. SIGCHI

Conference on Human Factors in Computing Systems,

pages 3247–3256. ACM.

Fairchild, M. D. and Johnson, G. M. (2004). iCAM frame-

work for image appearance, differences, and quality.

Journal of Electronic Imaging, 13(1):126–138.

Ferreira, N., Poco, J., Vo, H. T., Freire, J., and Silva, C. T.

(2013). Visual exploration of big spatio-temporal ur-

ban data: A study of new york city taxi trips. IEEE

Transactions on Visualization and Computer Graphics,

19(12):2149–2158.

Hillier, B. (2002). A theory of the city as object: or, how

spatial laws mediate the social construction of urban

space. Urban Design International, 7(3):153–179.

Hillier, B. (2007). Space is the machine: a conﬁgurational

theory of architecture. Cambridge University Press,

Cambridge.

Jiang, B. (2007). A topological pattern of urban street net-

works: universality and peculiarity. Physica A: Statis-

tical Mechanics and its Applications, 384(2):647–655.

Keim, D. (2000). Designing pixel-oriented visualization

techniques: Theory and applications. IEEE Transac-

tions on Visualization and Computer Graphics, 6(1):59–

78.

Kindlmann, G., Reinhard, E., and Creem, S. (2002). Face-

based luminance matching for perceptual colormap

generation. In Proceedings of the conference on Visu-

alization, pages 299–306. IEEE Computer Society.

Kropf, K. (2009). Aspects of urban form. Urban Morphol-

ogy, 13(2):105–120.

Liu, Y., Zhang, D., Lu, G., and Ma, W.-Y. (2007). A sur-

vey of content-based image retrieval with high-level

semantics. Pattern Recognition, 40(1):262–282.

Marshall, S. (2004). Streets and patterns. London and New

York: Spon Press.

Marshall, S. (2008). Route structure analysis: A system of

representation, calculation and graphical presentation.

Working Paper.

Mittelst

adt, S., J

ackle, D., Stoffel, F., and Keim, D. A. (2015).

ColorCAT: Guided Design of Colormaps for Combined

Analysis Tasks. In Proc. of the Eurographics Confer-

ence on Visualization (EuroVis 2015: Short Papers),

pages 115–119.

Mittelst

adt, S., Stoffel, A., and Keim, D. A. (2014). Meth-

ods for Compensating Contrast Effects in Information

Visualization. Computer Graphics Forum, 33(3):231–

240.

Oelke, D., Janetzko, H., Simon, S., Neuhaus, K., and Keim,

D. A. (2011). Visual boosting in pixel-based visualiza-

tions. In Computer Graphics Forum, volume 30, pages

871–880. Wiley Online Library.

Omer, I. and Zafrir-Reuven, O. (2010). Street patterns and

spatial integration of israeli cities. The Journal of Space

Syntax, 1(2):295.

Pettit, C., Widjaja, I., Russo, P., Sinnott, R., Stimson, R., and

Tomko, M. (2012). Visualisation support for explor-

ing urban space and place. In XXII ISPRS Congress,

Technical Commission IV, volume 25.

Porta, S., Crucitti, P., and Latora, V. (2006). The network

analysis of urban streets: a primal approach. Environ-

ment and Planning B: Planning and Design, 33:705–

725.

Shneiderman, B. (1996). The eyes have it: a task by data

type taxonomy for information visualizations. In Visual

Languages, 1996. Proceedings., IEEE Symposium on,

pages 336–343.

Tian, J., Ai, T., and Jia, X. (2012). Graph based recogni-

tion of grid pattern in street networks. In Advances

in Spatial Data Handling and GIS, Lecture Notes in

Geoinformation and Cartography, pages 129–143.

Turner, A. (2001). A program to perform visibility graph

analysis. In Proceedings of the 3rd Space Syntax Sym-

posium, Atlanta, University of Michigan, pages 31–1.

Vaughan, L., Jones, C. E., Grifﬁths, S., and Haklay, M. M.

(2010). The spatial signature of suburban town centres.

The Journal of Space Syntax, 1(1):77–91.

von Landesberger, T., G

orner, M., Rehner, R., and Schreck,

T. (2009). A system for interactive visual analysis of

large graphs using motifs in graph editing and aggrega-

tion. In VMV’09, pages 331–340.

von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer,

J., van Wijk, J., Fekete, J.-D., and Fellner, D. (2011).

Visual analysis of large graphs: State-of-the-art and

future research challenges. Computer Graphics Forum,

30(6):1719–1749.

Wang, Z., Lu, M., Yuan, X., Zhang, J., and Van De Wetering,

H. (2013). Visual trafﬁc jam analysis based on trajec-

tory data. Visualization and Computer Graphics, IEEE

Transactions on, 19(12):2159–2168.

Ware, C. (1988). Color sequences for univariate maps:

Theory, experiments and principles. IEEE Computer

Graphics and Applications, 8(5):41–49.

Wheeler, S. M. (2008). The evolution of built landscapes in

metropolitan regions. Journal of Planning Education

and Research, 27(4):400–416.

Yan, X., Zhu, F., Yu, P. S., and Han, J. (2006). Feature-

based similarity search in graph structures. ACM Trans.

Database Syst., 31(4):1418–1453.

Yang, B., Luan, X., and Zhang, Y. (2014). A pattern-based

approach for matching nodes in heterogeneous urban

road networks. Transactions in GIS, 18(5):718–739.

Yang, T. and Hillier, B. (2007). The fuzzy boundary: the

spatial deﬁnition of urban areas.

StreetExplorer: Visual Exploration of Feature-based Patterns in Urban Street Networks