Urban Data Analytics application. Visibility and audience by Spatial Networks Analysis

Álvaro Cosidó López

MS Design by Data, Ecole des Ponts ParisTECH, Paris

Abstract. The visibility and the study of the audience in urban environment has been always a

useful indicator for Real-State and advertising decisions between others. While traditionally this

kind of analysis requires a fieldwork, or a huge computation times, in last decades the

democratization of computational power have allowed to analyse the urban morphology to

predict the people urban displacements providing indicators to help marketers optimize their

locations decisions. The objective of this research is to developpe a tool which allows measure

the visibility and the audience for an urban element or location (buildings, façades, …) by the

study of the visibility properties with a three-dimensional ray traced analysis and the audience

by the last advances in Spatial Network Analysis.

Keywords. Spatial Network Analysis, Street network, Shortest path, Gis, Graph theory,

Audience, Visibility, Isovist, Ray casting, application, Urban Data analytics.

1. INTRODUCTION

As we see in the digital world, the economic value of the

advertising is related to the amount and type of audience it

can impact on. In the same way, measuring the visual impact

on urban elements or counting the reach audience we can

estimate the value of the built reality and decrease the risks

in business location decisions or physical advertising

emplacements.

Visibility, beyond the primary understanding as a static

quality of a location, have to also be considered with a

temporal dimension and quantified by the impacts than a

location can receive during a period. This definition links the

visibility with the mobility and the audience. This last field of

study, has enjoy a huge transformation since the emerging

of smartphones, giving for the first time the opportunity to

switch from a statistics demographic field to an accurate

technique where the location and the study of the citizens

can be done in real time. This advance has caused the

emergence of a big industry in the data driven decisions

consulting.

This sensible data is often recollected with some

controversial methods and use to have a very restrictive use.

Between others, that’s why the challenge for the

development of this application is how to simulate a

accurate simulations of mobility without that data, leaning

on the hypothesis than in every urban morphology there is

an intrinsic behaviour of displacement that we can analyse.

Spatial analysis and most exactly network analysis allows us

to predict that mobility behaviour. When we look at a map,

it intrinsically begins to turn into information by finding

patterns, evaluating trends, and making decisions. This

process is called spatial analysis and consists of what your

eyes and your mind do naturally when you look at a map.

However, patterns and relationships are not always so

obvious when looking at the map. Spatial analysis has had

important advances in last decades because of the

improvements in computational capacities but also because

of the optimization of the calculation algorithms that

provides the indicators.

Often there is too much data to move around the map and

to present it coherently. The way you display the data on the

map can change the patterns you see. That’s why, when the

interpretation of some indicators become complex to

manage and to understand, the creation of applications or

tools has sense to communicate those results. The prototype

developed in this work allow users to quantify the patterns

and relationships of the data, and display the results as

maps, tables and graphs, allowing to answer questions and

make decisions with more than just a visual analysis. This

paper presents a research behind the development of a tool

to quantify the audience and the visibility of a specific

location in the urban space.

2. BACKGROUND RESEARCH

2.1 MOTIVATION

The democratization of open data that is taking place in this

last decade gives access to the development of new services

in urban decision making. Cities and the behaviour of their

inhabitants has been studied for long time, but certain

disciplines with routes not as distant like the study of city

networks, had required an inaccessible fieldwork in their

time which, nowadays, the massive geolocation of data can

validate, with real and updated data of the fields studied.

The world of real estate has not been left out of these

advances, valuation algorithms have burst onto the market

in recent years mapping the value of the city. It is at this point

the exploration of new indicators or the improvement of

existing ones, arouses great interest. This work pursues an

automation of some of these indicators through work with

open data and the exploration of analysis methodologies

based on the intrinsic value of a location based on the impact

and presence to which it is exposed.

2.2 LITERATURE REVIEW

2.2.1 VISIBILITY

The visualization field provide us with important indicators

about the performance of a location for a concrete activity.

While some activities attract audience, some others require

a minimum presence or impact. How can we measure the

visibility of a proposed location? The relation between the

visibility and the urban space morphology has been

investigated from many architects and designers like

Appleyard et al. (1964), Benedikt (1979), Kevin Lynch (1976)

or Tandy (1967). This last one introducing the term Isovist

which is the method to measure a volume of space that is

visible from a single point in space or as M.L.Benedikt points

out: “An isovist is the set of all points visible from a given

vantage point in space and with respect to an environment”

[1].

Most recently, in the past decade, some other researches

have enjoyed the computational advances to developpe

calculation routines, methods and frameworks in visibility

analysis. Some of them like *Turner, e.a. (2001) show how a

set of isovists can be used to generate a graph of mutual

visibility between locations. The measurement of local and

global characteristics of the graph, for each vertex or for the

system as a whole, is of interest from an architectural

perspective: to describe a configuration with reference to

accessibility and visibility, to compare from location to

location within a system, and to compare systems with

different geometries. Bilsen and Stolk (2007) have

established an open framework for Isovist-Based Visibility

Analysis (IBAV) for urban design and planning. They

performed the visibility analysis on several study cases,

ranging from modern to traditional, rural to urban, and

private to public to address and exemplify IBVA and the

framework. They have gained rich results and also proposed

a relatively complete framework for analysing visibility on

urban streets. Similarly, Weitkamp (2010) has proposed a

five-step method for landscape visual openness by using

Isovist Analyst. Further, Peters etc. (2015) use medial axis

transform (MAT) for visibility analysis in a built environment

including trees and buildings.

In almost all the explored methodology’s, the analysis runs thanks to a ray casting. The term was first used in computer

graphics in a 1982 paper by Scott Roth to describe a method for rendering constructive solid geometry models.[2]

Ray casting can refer to a variety of problems and techniques, but is the general problem of determining the first object intersected by a ray, [3] which interest to this work. Although "ray casting" and "ray tracing" were often used interchangeably in early computer graphics literature, more recent usage tries to distinguish the two. The distinction is that ray casting is a rendering algorithm that never recursively traces secondary rays, whereas other ray tracing-based rendering algorithms may do so.

2.2.2 AUDIENCE

But visibility become a larger concept when is not only

defined by the space properties from where can being seen

but by the probability to being seen. The question this

calculation pursue is how many people will probably pass

through a location? and bring us to a reciprocal question,

how can we read and predict the mobility of the citizens

into the city?

Predict the human behaviour has been for long a big

challenge. A well-known method to do it it’s by the study of

the city patterns by the space syntax methods which, by

computational routines, analyse the connexions in urban

network morphologies. In the words of Hillier (1987, page

363): ``Space syntax ... is a set of techniques for the

representation, quantification, and interpretation of spatial

configuration in buildings and settlements. Configuration is

defined in general as, at least, the relation between two

spaces taking into account a third, and, at most, as the

relations among spaces in a complex taking into account all

other spaces in the complex. Spatial configuration is thus a

more complex idea than spatial relation, which need invoke

no more than a pair of related spaces.'' [4].

This discipline, based on the graph theory, has been limited

by the computational capacity from the beginning. Since the

Euler puzzle was published in the 18th century, spatial

analysis methods have been studied as predictors for

different urban phenomena’s like the flow of pedestrian

traffic on city streets (Hillier et al., 1987), and the distribution

of retail and service establishments in urban environments

(Porta et al., 2005; Sevtsuk, 2010).

Is in the development of centrality metrics where this field

of study have more relevance to this exploration. Network

centrality measures are mathematical methods of

quantifying the importance each node in a graph. As the

name implies, centrality metrics focus primarily on how

centrally each graph element is located with respect to the

surrounding elements. Graph centrality metrics are

analogous to spatial accessibility measures, but applied on

network rather than Euclidian space (Bhat et al., 2000) [5].

Using the Urban Network Analysis Toolbox developed for

Andres Sevtsuk and Michael Mekonnen for the platform

ArcGIS and after for Rhinoceros, we have easy access to a

preformed routine which provide us the algorithms to apply

for some centrality metrics like Reach, Gravity index,

Closeness, Straightness and Betweenness.

In the same way almost all the visibility analysis explored has

been based in ray-casting, in network syntax also exist a

common root, the shortest paths. The main calculation

behind the algorithms of urban centrality are based in the

“Shortest paths” as an optimum path or geodesic in a graph.

Conceived by computer scientist Edsger W. Dijkstrain in

1956 [6], Dijkstra's algorithm is an algorithm for finding

the shortest paths between nodes in a graph. Based on this

one, Peter Hart, Nils Nilsson and Bertram Raphael of

Stanford Research Institute expand the algorithm in 1968

developing the A* algorithm [7], which works faster than the

Dijstra’s one. Today, new researches, like the work of Pirouz

Nourian (2016) in his Grasshopper Plugin “Cheetah the

configurbanist”[8], include another accurate calculation by

introducing the “Easiest path” which considers the physics of

walking/cycling mobility as well as the cognitive aspect of

human navigation.

2.6. HYPOTHESIS

The hypothesis behind the development of this application

is to consider that it is possible to achieve the suitability of a

location for a concrete activity based on the study of the

visibility and the movements of their citizens. It is also

considered in the hypothesis, that the routes in the city try

to minimize time and energy, so the work with shortest

walks algorithms is justified to predicts the behaviour of

urban displacements. Obviously, that will not consider many

other aspects that make the people chose one way or

another, like sun conditions, dangerousness, commercial

routes or urban comfort, between others.

3. OBJECTIVE

The objective is the creation of an application, supported by

Grasshopper and Rhinoceros which, by a requested location,

automatize the calculation and the export of results of urban

indicators of visibility and audience to help in business

location decisions.

4. METHODOLOGY

CODING A PROTOTYPE

The application it’s prototyped in Grasshopper. This software allows to easily manipulate data and geometries in a visual programming canvas while the results are sent directly to a 3D viewport (Rhinoceros). Most of the software’s oriented to visibility analysis and Spatial Network Analysis rarely support 3D modelling or are prepared to modify this one in design phases. This restriction limits a lot the methods in both. Some of the well-known

analysis software such as ArcGIS, QGIS or gvSIG are in that group. They have also a problematic workflow in the export and import of models or data from other tools or formats, and it’s not easy to change or create new routines of calculation out of the default ones. The software choice is also pertinent because grasshopper allows, through different plugins, to code every step, from the data interpretation to the design of the user interface, developing a “ready to use” functional application and defining all the data-routines for a future optimized version. The code development is divided in three main sections: • Urban Datasets • Analytics. Visibility and Audience • Visualization of export of results 4.1 URBAN DATASETS The objective in this first section it’s to automate the generation of one part of the city, in a virtual 3D model, by writing only an address. A most accurate data will provide better 3D model and proper results in the analysis, that’s the reason why, for each city, a first research should be done to find the better data sources or elaborate the own one. Urban data is every day more developed, cities improve and expand their own stock of open data constantly. The application, based on the city of Paris, needs firstly data about physical features like: • Topography • Building footprints, sidewalk, roads, parks. • Building height, number of floors. This data, usually is not enough to build an accurate virtual model but offer a simplified version that sometimes is enough for visibility analyses. To recreate the city other techniques, like cloud points, offers a higher level of definition. A cloud point can be obtained, between others, by photogrammetry or by 3D scanning. Obtaining Cloud points seems to be the best option, but have his own associated problematics when is oriented to urban fields, in technical, because of the size and complexity, and legal, because of the methods to obtain it and the privacy of certain areas and pedestrians. Is also complicated to discretize the elements in a cloud points to associate different lectures. For example, differentiating between a road and a sidewalk. That’s why, between others, the application uses a simplified version with the urban datasets. At the same time the city is virtually build, a data about the uses and nomenclature of the different areas is associated to the 3d elements as attributes in the file. • Urban addresses. • Distribution of uses (residential, recreational, transport…) • Metro stations, Bus stations, (others) All this data together will help us to make a model of the city, to obtain the different lectures. In this work, based in the city of Paris, the Urban data is downloaded directly from the Parisdata (Mairie de Paris), where files are offered in different formats: Flat (CSV, JSON, Excel) and Geographic (GeoJSON, Shapefile, KML). The shapefile format is a digital vector storage format for storing geometric location and

associated attribute information. The shapefile format is simple because it can store the primitive geometric data types of points, lines, and polygons together with data attributes. Representation provides the ability for powerful and accurate computations. The format consists of a collection of files with a common filename prefix, stored in the same directory. The three mandatory files have file name extensions listed in the table 1.

Extension Definition

.shp Shape format (geometry)

.shx Shape index format

.dbf Attribute format

Table 1: Files definition in a shapefile Shapefiles use to be referenced to geographic coordinates in World Geodetic System 84 (WGS84) with a Latitude, longitude and height, expressed in degrees, minutes and seconds. To easily operate with those shapes, the coordinates are transformed to a cartesian ones in a Universal Transverse Mercator coordinate system (UTM), expressed in meters with cartesian coordinates in x and y axes. To perform this transformation a projection file of our Spatial system (SRID/EPSG) is needed. For Paris is the EPSG:2154. These projections take time because of the amount of data to work with. To reduce this time and accurate computations, the shapes are internalized in cartesian coordinates. This is done by reading first the shapefile with the plugin @it, to them project the shapes with Mosquito plugin using the PRJ file requested for the EPSG:2154 in this case. Once the shapes are projected we internalize all the data in a list for each shapefile. Keeping the same index to be able to reference the rest of attributes. By writing the address, and searching the similar one in the address internalized set with the grasshopper component “similar member in a set”, it’s possible to easily get the coordinates of the requested location. By measuring the distance form this coordinates to the rest of elements by different methods depending the kind of analysis, a selection of shapes and attributes is made. 4.2 ANALYTICS. VISIBILITY AND AUDIENCE Once the urban data is ready to be used, the calculation routines for the different analysis are coded to start with the essential inputs. For each category the urban data is interpreted in a different way, by a reconstruction of the physical context, in the visibility analysis, and with a graph reconstruction for the audience analysis. 4.2.1 VISIBILITY Visibility in urban context, interpreted in a physical dimension can be analysed by different methods, from quantitative to qualitative. The application uses well-known methods based on ray-casting to measure visibility indicators in two directions, from the target shape to the ground points and conversely, but also developpe a methodology to study the visibility considering the features of the human sight.

Three inputs are necessary to run the visibility analyses: • Location: addressee or latitude and longitude. • Distance: Integer (meters). • Target: Point / Surface / Mesh. Location, as it explains before, give us a point from where to choose which shapes and attributes will be selected from the internalised file. This is where the distance plays a role. Distance its defined by type of analysis and the target size, but for this version some establish intervals are fixed.

Type of analysis Distance (m)

Short (Read) d < 20

Medium (Recognition) 20 < d < 150

Large (Landscape contemplation) d > 150

Table 2: Default distances by type of visibility analysis. Distance and location provide to the script the capability to build the desired part of the city. Topography is modelled as a mesh directly from the Ladybug_Terrain generator component from Ladybug plugin. This component uses Google Maps API to achieve elevation data and satellite images of the terrain. Over this mesh planar shapes from footprints of buildings and other urban elements are projected. Road can be obtained by cutting this mesh with sidewalk and building footprints. Sidewalks in similar way, and buildings by extrude the projected curves directly as low density meshes with a “m+Offset/Extrude” component from M+ plugin with their corresponding height. Other shapes like vegetation can be directly located in the projected point over the terrain. After that we are only missing the target surface which at this point, its modelled manually. The achieved result is presented as is shown in the figure 1. Following analyses, are fed with four inputs, which differentiate between road and sidewalk to offer different lectures of the results: • Target mesh • Road mesh • Sidewalk mesh • Context mesh

Figure 1: 3D model obtained for 78 Rue Compans adresse.

4.2.1.1 VISIBILITY SCOPE ANALYSIS This analysis works under the hypothesis than one target point A is visible from one point B if there is no other object in between the straight line from A to B. The study gives how much ground surface is visible from each point of the target, by connecting those points to all the centre points of each face that constitute the ground mesh and evaluating if the context collides with these lines. Similar analysis its done in the opposite way, investing the target and the ground inputs, to achieve the amount of surface visible from each ground point.

Figure 2: Ray-casting method for scope from target to

ground (1), and from ground to target (2). Usually urban analysis needs vast areas to analyse, that’s way to optimize computations is crucial. This ray tracing method has been optimised in the Ladybug plugin by the “Ladybug View Analysis” component. Adjust the precision of the target or ground mesh by changing the grid size of the mesh is possible but is important to take care because of the calculation time it will take. Times are listed in the table 3.

Target mesh (faces) Ground mesh (faces) Time (s)

364 1006 3.2

364 3672 10.7

1590 3672 49.4

Table 3: Computation time in ray tracing analysis spent by a computer with the features listed in Table 7.

As shown in the figure 3, different ground surfaces can be analysed, sidewalk, road or all the ground surface, to get more precise conclusions. The output of this component gives the percentage and the total area visible from each point on the target or the ground mesh to their respective. Values are projected numerically on faces of the evaluated mesh and graphically by a colour gradient.

Figure 3: Results visibility scope analysis by ground selection.

4.2.1.2 FIELD OF VIEW ANALYSIS

This analysis evaluates the percentage of our field of view

that is occupied by the target shape. A pair of healthy human

eyes has a total field of view of approximately 200 degrees

horizontally, about 120 degrees of which are shared by both

eyes, giving rise to what's known as binocular vision and 135

degrees vertically, (though these values tend to decrease

with age). This is because both of our eyes are positioned on

the front of our heads, as opposed to the sides. This domain

builds the human cone of vision, which is defined by four

values, the vertical angle+, the vertical angle-, horizontal

angle +, horizontal angle – and the distance limits. Different

activities define different cones of vision. For this prototype

an open parameter allows to decide between the options in

the table 4.

Figure 4: Human field of vision.

Activity V.A. (+)

V.A. (-)

H.A. (+)

H.A. (-)

Distance limit

Human 50 ° 70° 62 ° 62 ° 10 m

Peripheral vision 60 70 60 60 10 m

Outdoor 90 15 180 180 100 m

Sign recognition 30 40 15 15 10 m

Word recognition 30 40 5 5 10 m

Table 4: Human cone of vision by activity. The analysis is performed by dividing the cone of vision in equal area points to create a set of vectors. Those ones will be thrown from each face point of the ground mesh to the shape, rotating to find the biggest value. There are many ways to divide up the sphere into small segments, some methods have been developed in environmental fields for sky subdivisions, like the Reinhart 580, the Tregenza method or the most popular 145 segment equal-area subdivision.

Figure 5: Sky subdivision using 145 evenly-distributed virtual light sources.

But human eyes have his own resolution, and we can adjust for each activity a different number of division. This capacity is determined by the Angular resolution which is given by the Rayleigh criterion:

Θ = 1.22 λ

Drad

Where λ is the wavelength of light and D is the diameter of the lens aperture. For a human eye, in good day light conditions, the pupil diameter is 3mm (average), and gives an angular resolution of approximately 0.02° or 0.0003 radians. This corresponds to 0.3 m at 1km of distance, and it’s the limit where if two light spots are closer than that distance, we can’t distinguish one from the other. Dividing the cone of vision in segments with this resolution we can have a perfect calculation. But the number of vectors resultants will be too big to calculate. Depending the kind of analysis, we divide a cone of vision in different resolutions. Table 5. The division is done by a calculation where a sphere is populated randomly with a fixed number of points. From each point some secondary spheres are created with the average of the distances that connect the closest points in the sphere (8 points) as radius. By fixing, with a string, the distance from the centre point with the radius of the primitive sphere we calculate a sphere equidistant distribution where a kangaroo physics simulation avoids the collision between them, as is show in the figure 6.

Table 5: Number of points by angular resolution.

Figure 6: Cone of vision vectors.

Once the vectors of the field of view are calculated, (by default the application will use the 595 vectors) the analysis is done with a ray tracing method as it is made in the scope analysis. The difference here is the amount of rays to

calculate makes the analysis long. A loop with Anemone plugin is configurated to analyse point by point. The output, gives the percentage of the field of view rays that impact, without obstruction, on the target shape. Values are projected, like in the visibility scope analysis, numerically with the percentage over the ground points and graphically by a colour gradient over the ground mesh. Figure 7.

Figure 7: Field of view analysis results.

4.2.2 AUDIENCE For all these calculations what we need first to create the graph. All the analysis are generated on the graph of the city, so it is not necessary to model the city, but simply its representation as a graph. In graph streets becomes lines connected with nodes. From different sources we feed the same with information on urban uses, built surface, access to homes, commercial spaces, infrastructure points, etc. to enrich the graph and get closer to rigorous results. Once generated, the metadata or attributes, represented by points in the graph, are internalized for greater fluency. The biggest difficulty lies in the creation of a clean graph in which segments and nodes are correctly connected. Constructing a street network that is topologically clean and valid is not trivial dealing with real datasets such as OpenStreetMap. In fact, the subject of constructing topological data models of networks is rigorously studied specially for automating such procedures, e.g. see the methods for constructing network topological models for traffic simulations as in (Nielsen, OA, Israelsen, T & Nielsen, ER, 1997) [13]. To establish topological adjacency between points and get a useful graph of the city of Paris, the precision of the road is adjusted by a subdivision process, splitting the street polylines at junctions, and inserting nodes at junctions. By removing duplicate points, a list of vertices is formed and by finding incident lines to these vertices, a list of topological edges between these vertices is constructed. The lines are drawn in both directions to allow for construction of a Point-to-Point directed network. The edges of this network become the nodes of the Line-to-Line graph representation that will be used for finding Easiest Paths

AR (rad) Number of points in sphere

Number of points in Field of view. (Human)

0.15092 500 151

0.07768 2000 595

0.059285 4000 1211

0.03957 8000 2422

0.0003 x n

Once the graph is ready, the information about the use of each node and segment must be add. The weight of each point to evaluate, for example, can be defined by the number of people that emits or hosts. This approach is carried out by dividing by 3-meter planes on the z-axis, of the constructed volumes of the city, accessible through the resources used in the visibility studies. Other possible weight can be added to the segments, by default is set the distance of each segment. More data will help to elaborate hypothesis to orient the results to a segment of population or to evaluate specific sectors. Like in the visibility analysis, in this category its necessary to three inputs are necessary to run the visibility analyses: • Location: addressee or latitude and longitude. • Distance: Integer (meters). • Target: Point / Surface / Mesh. Location, as it is explained before, give us a point from where

to determine the part of the graph and the attributes the

analysis needs. This is where the distance plays a role.

Distance its defined by type of analysis and is connected to

the human capacities. Statistically, people move at the

average speeds listed in the table 6. This allows determinate

the distances to apply in the calculation of the different

centralities.

Activity Speed

Walk 5 Km/h

Jogging 6 Km/h

Run 10 Km/h

Table 6. Human speed average by activity.

4.2.2.1 REACH

Knowing the average of distances people use to walks in

cities before use another transport, is possible to trace the

number of people that have at least one of their possible

paths passing through a proposed location. That indicator,

in network analysis, is called Reach centrality. By definition,

the reach centrality of a node “I” in a graph “G” at a search

radius “r”, describes the number of other nodes in “G” that

are reachable from “i” at a shortest path distance of at

most “r”. It is defined as follows:

Where d[i,j] is the shortest path distance between nodes “i”

and “j” in “G”, and W[j] is the weight of node “j” (Sevtsuk

2010).

In the application, emitters act like nodes, and the location

is the i value. By the component “Shortest walk” paths

distances are measured from the closest node of the graph

from emitters, and the weight is the number of people

estimated in each one of them. The result is the sum of

emitters and the average distance of all the paths to the

target location. Figure 8.

Figure 8: Reach analysis

4.2.2.2 CLOSEST FACILITY TO SUBWAY STATION

This analysis evaluates how many shortest walks, from each

emitter of my audience scope, pass through the target

location when walks to the closest station of each subway

line. This is done, first, calculating the distance of the

shortest walks from “L” to all the stations and selecting those

who are in less than a distance (1km by default). After,

emitters are grouped by their closest station per subway

line. At the same time the paths are created. By comparing

if the segment of the target location is in the paths created,

is possible to select the paths that pass through the target

location. Finally, each path is multiplied by the number of

people coming from each emitter, giving as result the

number of times each segment of the graph is crossed by a

shortest walk. Figure 9.

Figure 9: Closest facility to subway station

4.2.2.3 BETWEENNESS (WALK)

Other useful indicator is betweenness, who estimate the

potential of passers-by at different locations of the network.

By definition, the betweenness centrality, of a building “i” in

graph “G” estimates the number of times “i” lies on shortest

paths between pairs of other reachable buildings in “G” that

lie within the network radius (Freeman 1977). If more than

one shortest path is found between two buildings, as is

frequently the case, then each of the equidistant paths is

given equal weight such that the weights sum to unity. It is

defined as follows:

Where d[i,j] is the shortest path distance between nodes i

and j in G, and W[j] is the weight of node j.

This analysis uses the betweenness to evaluate the

behaviour of the previous defined reach in short walking

displacements (500m) in their surroundings. By changing the

radius “r”, it’s possible to predict different traffic models

(walkers, drivers, bikes, etc). The result gives how often a

segment of the path is crossed by a shortest path coming

from all the reach to all their surroundings in a radius along

shortest paths. To perform this calculation each emitter

point analyse the destinations (in the city, not only in the

reach) less than 500 meters away, using the shortest walk

component. By applying the same method with all the

emitter points and multiplying over each path the weight of

audience, is possible account the number of times each

segment is repeated. Figure 10.

Another interpretation of this analysis is done by calculating

for each point, the percentage of shortest paths that pass

through the target location.

Figure 10: Betweenness indicator (Top view)

Figure 10’: Betweenness indicator (isometric view)

4.3 VISUALIZATION AND EXPORT OF RESULTS

Because the results of the application are oriented not only

for people from technical fields, an exportable document is

designed, foreseeing what users might need to consult and

ensuring that is enough compressible. The application

exports two different documents, one for each category,

visibility and network analysis.

Each one of the analysis runs after press a Boolean toggle.

The times each analysis spend depends largely on the size of

the target and ground mesh in visibility analysis and in the

amount segments is divided the graph in the network

analysis. By setting the default parameters, the times each

analysis spend is listed in the table 8. When all the analysis is

over, by only pressing a button, the “Ladybug captureview”

component export two images, automatically generated

(Figures 11 & 12). Default export set in png format and

3295x2480 pixels size (300 dpi in A4).

4 TESTING THE APPLICATION

Once the application its coded, a test is done to evaluate its

performance and detect weak points. Running on

Rhinoceros 5.0 and Grasshopper 0.9.0076 in a computer

with the features of the table 7. Any internet connexion is

needed, because all the datasets have been downloaded and

will be read in local.

OS Edition Windows 10 Home

Processor Intel(R) Core(TM) i7-4720HQ CPU @2.60GHz 2.60GHz

Installed RAM 16,0 GB

System type 64-bit operating system, x64-based processor

Table 7 : Test computer features

Fixing the default parameters, to run the application is only

necessary the next inputs:

• Address: 22 Rue Coquillière, 75001 Paris.

• Target shape: (Only for visibility analysis)

Results are exported graphically and numerically in the

figures 11 and 12. To evaluate the fluidity of the application

the computation times of each of the analyses is listed in the

table 8.

Categories Analysis Time (s)

VIZ

Gis 45.2

Visibility Scope 6.5

Field of view 750.4

NET

Gis -

Reach 14.2

Closest facility 22.3

Betweenness 1452.0

Table 8. Measurements of computation time by analysis

performed. (All ground)

Figure 11: Visibility export

Figure 12: Audience export

5. OBSERVATIONS

After the experimentation phase there are unforeseen

events that can compromise the correct functioning of the

application. They are listed below:

• The first and most important observation is that the fluidity

of the application is compromised by the calculation time

used by certain parts of the code. As seen in the table 8, two

critical points appear during the process: the modelling

phase of the city in the visibility analysis, and the calculation

of Betweenness in the network analysis section. Both related

to the calculation limitations of the development platform

used.

• In visibility studies, topography does not have a sufficient

definition to simulate precise results, especially in areas with

a steep slope. There is also a limitation set by Google Maps

of 2500 requests per day, which could compromise

availability. On the other hand, both vegetation and other

urban elements, which greatly modify the visibility results,

are not considered.

• The introduction of the objective shape in the visibility

studies is manual, forcing us to know the modelling software

and to build a different target for each location, which limits

the automation of the process.

• In network studies, the calculation of the weight of each

emitter is very imprecise. It offers population estimates that

do not always correspond to the occupational reality.

Another point in this category is the necessary periodic

review of the dataset.

Those remarks suggest that the application don’t offer

enough accuracy in some of the results and some parts will

not be able to automatize.

6. CONCLUSIONS AND WAY FORWARD

The development of new value indicators in the real estate

world, is, since just a decade, a rather hectic exploration field

with the arrival of automatic valuation algorithms and Big

data analysis. In this line, the created application seeks to

add new indicators in the calculation of the value of the real

estate stock such as visibility and audience. Those require

certain levels of precision to be considered, since they work

with circumstantial and statistical hypotheses.

Visibility studies, for example, require a minimum of

precision in modelling the environment to provide reliable

results. The simplification made by the prototyped

application offers results of comparative interest, since the

rest of the city is modelled in the same way, but does not

offer values that are sufficiently adjusted to reality. Another

methodology in the urban 3d generation should be explored.

Point clouds, for example, represent a qualitative leap that

will provide sufficient basis to apply more precise studies. In

the field of the study of the audience and mobility in urban

contexts, mobile devices have meant a before and after, but

that does not render obsolete the network syntax studies

explored in this work, it extends it. The application therefore

foresees a broader development in this orientation.

• The future application seeks to establish a rapid recovery

of the different calculation hypotheses by internalizing the

longer calculations, to offer the values instantaneously. For

the rest, we will study a way to optimize the calculation

routines for other more efficient words in computing times.

• The division of uses and surfaces of the city will be improved by the crossing of new databases. • Add sociodemographic data, allowing to customize the study of analysis oriented to sets. • Add data by commercial activities that allow the development of precise market studies regarding the relationship with competitors. • Implement the study by isochrones, improving the interpretation of the audience and the movement in the urban fabric. Finally, the possibility of converting the application into an open online service will be explored, where the internalized calculations can be consulted directly by entering the target location. ACKNOWLEDGEMENTS

The Autor would like to thank engineer Sebastien Perrault

for his valuable references and discussions and architect

Aymeric de la Bachelerie for the shared knowledge during

the internship in FBC.

REFERENCES

[1] Benedikt ML (1979). To take hold of space: isovist and isovist fields. [2] Woop, Sven; Schmittler, Jörg; Slusallek, Philipp (2005),

RPU: A Programmable Ray Processing Unit for Realtime Ray

Tracing.

[3] Roth, Scott D. (1982). Ray Casting for Modeling

Solids, Computer Graphics and Image Processing.

[4] Hillier B, Hanson J, Graham H, (1987). Ideas are in things:

An application of the space syntax method to discovering

house genotypes.

[5] Dijkstra, E. W. (1959). "A note on two problems in

connexion with graphs"

[6] Bhat C, Handy S, Kockelman K, Mahmassani H, Chen Q,

Weston L (2000). Development of an Urban Accessibility

Index: Literature Review.

[7] Hart, P. E.; Nilsson, N. J.; Raphael, B. (1968). A Formal

Basis for the Heuristic Determination of Minimum Cost

Paths.

[8] Pirouz Nourian, Samaneh Rezvani, Sevil Sariyildiz, Frank

van der Hoeven (2015). Easiest paths for walking and cycling:

Combining syntactic and geographic analyses in studying

walking and cycling mobility.

[9] Freeman L.C. (1977). A set of measures of centrality

based on betweenness.

[10] Andres Sevtsuk, Michael Mekonnen (2012). Urban

network analysis A new toolbox for ArcGIS.

[11] Crucitti P, Latora V, Porta S (2006). Centrality in

Networks of Urban Streets.

[10] Pirouz Nourian, Samaneh Rezvani, Sevil Sariyildiz, Frank

van der Hoeven (2015). Spectral Modelling for Spatial

Network Analysis.