Towards Highly Affine Visualizations of Consumption Data from

Buildings

Matthias Nielsen and Kaj Grønbæk

Department of Computer Science, Aarhus University, Aabogade, Aarhus, Denmark

Keywords: Interactive Visual Analytics, Spatial Visualizations, Energy Management Support.

Abstract: This paper presents a novel approach AffinityViz to visualize live and aggregated consumption data from

multistory buildings. The objective of the approach is to provide a generic but high affinity relation between

real buildings’ spatial layouts and the consumption data visualizations. Current approaches come short on

maintaining such affinity. This implies an avoidable cognitive load on users such as energy managers and

facility managers who need to monitor consumption and make decisions from consumption data. To

alleviate this we have transformed three conventional types of visualizations into highly affine

visualizations lowering the cognitive load for users. The contributions are: 1) Development of the

AffinityViz techniques featuring three generic designs of highly affine visualizations of consumption data.

2) Comparison of the affine visualizations with the conventional visualizations. 3) Initial evaluation of the

AffinityViz designs by expert users on real world data. Finally, the design challenges of AffinityViz are

discussed, including prospects for AffinityViz as a future tool for visual analysis of data from buildings.

1 INTRODUCTION

The research behind this paper took place in the

EcoSense project (EcoSense, 2014), where we study

human energy related behavior in a dorm living lab

equipped with multiple sensors continually

monitoring consumption data from the dorm

apartments (Blunck, H., et. al., 2013).

As energy and resource consumption data in

large buildings is collected at an increasingly

granulated level from sensors in modern buildings, it

is necessary to rethink how such data is visualized.

Although existing types of visualizations are

technically capable of visualizing high granularity

consumption data from multistory buildings, novel

visualization techniques are needed to create

visualization that cater to a broader spectrum of

professionals wanting to analyze such data. This

applies to use cases where building administrators

need to analyze and understand patterns in

consumption to better understand requirements for

infrastructure revisions or building upgrades.

Another use case is interventionists (researchers,

administrators, or others) who want to launch

initiatives to lower consumption and therefore need

to understand which parts of the building, or which

tenants, are evident targets. This means that

professionals from a wide range of disciplines could

need to analyze buildings consumption data, and that

they need to analyze varying types of data, such as

consumption of water, electricity, district heating,

etc.

In the design of AffinityViz, we exploit that

many multistory buildings such as office buildings

and apartment buildings have a simple recurring

physical layout across office/apartment size and

floor plans, by rendering a simplified 3D layout

plotted with data points representing single units

(apartments or offices) in the building. This results

in a novel visualization technique that leverages user

understanding of visualized data by retaining a

building’s spatial layout.

The paper is structured as follows. First, we

describe related work on conventional visualization

techniques as well as current state of the art in

visualizations of consumption data from buildings.

Second, we discuss how conventional visualization

techniques can be adapted to become affine

visualizations as well as a more radical highly affine

visualization. Third, we elaborate on the design of

AffinityViz –in terms of current implementation as

well as envisioned enhancements. Fourth, we

elaborate on the implementation of the current

prototype and lessons learned from testing it with

facility managers. Finally, we discuss AffinityViz

247

Nielsen M. and Grønbæk K..

Towards Highly Afﬁne Visualizations of Consumption Data from Buildings.

DOI: 10.5220/0005315102470255

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 247-255

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

and future work on tools for visual analysis of

consumption data.

2 RELATED WORK

This review covers conventional visualization

techniques appropriate for visualizing consumption

data from multistory buildings, examples of usages

of 3D data representation in information

visualization, examples of state of the art in spatial

and volume-based visualization, examples of

academic work in energy consumption visualization,

and a related architecture concept.

Cluster based heat maps (Wilkinson, L., et. al.,

2009) visualizes data in a matrix using color to

represent data values. They have wide-ranging

applicability and excel in visualizing ordinal data

while retaining hierarchies in the data. Jacques

Bertin (Bertin, J., 1969) discusses the use of 3D

topographic reliefs to visualize data from nations or

regions in effect creating 3D cartograms. Reliefs are

extruded to represent data values of topographic

areas and the reliefs themselves serve as

contextualization.

Perspective visualization have been explored as

general information visualization interface technique

(Carpendale, M. S. T., et. al., 1995). They discuss

in-depth different ways of handling distortion of

graphs when visualizing data in three dimensions.

Wright (Wright, W., 1995) has pioneered 3D

information visualization for applications in capital

markets. Wright constructs 3D scenes, plotted with

abstract 3D geometrical objects, which users can

navigate and explore. Wright’s work builds upon the

3D user interface design paradigm, Information

Visualizer, developed by Robertson et. al.

(Robertson, 1993).

Power BI for Office 365 (Microsoft, 2014) is a

plugin visualization tool for Microsoft Excel that

supports overlays on 2D maps, viewing maps from

tilted angles, creating a 3D view, and plotting data

with a geospatial reference onto the map as 3D

histograms. Other general tools for data visualization

include Tableau (Tableau Software, 2014), a BI tool

creation of interactive visualizations and dashboards.

Data-Driven Documents (Bostock, M., et. al., 2011)

is a multipurpose JavaScript library for transforming

datasets into web browser DOM elements.

The New York City Energy Usage Map

(Howard, B., et. al., 2012) is an interactive map that

plots energy usage on block and lot level in New

York City, creating a high detail cartogram. Data is

visualized as polygons that are colored according to

energy usage in terms of kWh per m

. A

contemporary practice of consumption data is to

create an interactive visualization dashboard that

visualizes resource consumption data in faceted

views. Examples include Lucid’s BuildingOS and

Building Dashboard (Lucid, 2014) and Buildings

Alive (Buildings Alive, 2014), all products using

composition of visualizations to support visual

analysis of consumption data for various settings.

Cube Lease (Cube Cities, 2014) visualizes entire

floor plans or single leases superimposed onto

renderings of the shape of large multistory buildings.

South Korean studio randomwalks has proposed a

futuristic architectural concept, Data Formation

(randomwalks, 2009), which interconnects the

resource consumption of inhabitants in a tall rise

building with their physical habitat in order to create

a symbiotic relationship.

3 DATA ABOUT BUILDINGS

In section 2, we saw examples of data with a

geospatial reference to locations. But, we did not

find any that relate data to the spatial layout of

buildings. Visualizing data with a geospatial

reference in a layout adhering to the geospatial

reference, such as the topographic reliefs discussed

in (Bertin, J., 1967), is a commonly used technique.

It creates a direct relation between the data and the

location of its origin and uses a familiar spatial

layout of territories rather than abstract textual

descriptors and graphs. The same argument can be

applied to visualizations of consumption data from

large multistory buildings – by retaining the spatial

layout of a building in a visualization of data from

the building, we use a familiar layout and lessen the

cognitive load on the user. It is, however, not a

straightforward to retain the spatial layout of a

building when visualizing data from a building.

One approach is to model true to a building or its

shell and visualize data using overlays. However,

this would limit the visualization to the particular

building and limited its generalizability. Instead, we

propose the AffinityViz techniques (Figure 2, Figure

4, Figure 6) to adhere to a simple model that retains

the spatial layout of a building and is generalizable

across multistory buildings with a simple recurring

layout as well as it is implementable in

programming environments that can render visual

elements. Using simple geometric objects and

shapes in a 3D scene is similar to the Wright’s

approach (Wright, 1995), but in AffinityViz the

spatial layout of the scene is a familiar reference,

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like the relief topographic (Bertin, J., 1967) only to a

building instead of a territory.

We have created three AffinityViz designs that

retain the spatial layout of a building through a

simplified model of the building. Each design

represents data differently, but derived from or

inspired by conventional visualization techniques.

Two are derived directly from cluster based heat

maps (Wilkinson, L., 2009) and area maps (Tableau

Software, 2014), and the third is inspired by bar

charts, but makes a radical leap beyond these. Below

we elaborate on the underlying assumptions of a

building and its consumption measurements before

we discuss and compare our designs to similar

conventional visualization techniques. The visual

representation in AffinityViz relies on that the real

building being analyzed has a comparatively simple

layout. This excludes certain types of large buildings

that have complex shapes such as the Sydney Opera

House and the Gherkin in London.

3.1 Data from Multistory Buildings

Consumption data in large buildings can be gauged

for a number of resources. For AffinityViz, we

assume consumption data is a continuously

measurable resource, such as electricity

consumption, gas usage, district heating, etc. The

resource consumption itself is assumed to take place

in a particular unit out many similar units – e.g. an

apartment or office. These units will have a spatially

significant location in the building in the form of a

[floor, room] symbolic coordinate. Floors, we can

assume, are ordinal, meaning that they are categories

of data that have an interrelationship that can be

ordered – i.e. floor 12 is a higher floor than floor 11.

Rooms, on the other hand, can only be assumed to

be discrete, meaning that may or may not follow a

spatially sequential order.

3.2 Foundations of AffinityViz

We have developed three AffinityViz designs –

AffinityHeat, AffinityArea, and AffinityBar – by

exploring strengths and shortcomings in

conventional visualization techniques applicable for

visualizing building consumption data. Here we

discuss our three AffinityViz designs in relation to

the founding conventional visualization techniques.

Legends are omitted to emphasize the visual

representations and all examples visualize the same

data ordered in the same way.

3.2.1 From Heat Map to AffinityHeat

The cluster based heat map (Wilkinson, L., et. al.,

2009) is used for visualization three dimensional

data in a uniformly distributed 2D matrix of fixed

size rectangles with color or color intensity for

conveying data values. Data points in a heat map can

vary greatly in granularity from high granularity

visualizations showing gradual transitions to low

granularity categorical steps between boxed data

points. Either way the layout of a heat map is

commonly a meaningful ordinal layout of e.g. geo

coordinate based data of high or low granularity.

Figure 1: Heat map of building consumption data. Created

with Tableau (Tableau Software, 2014).

The ordinal layout of the heat map makes it

suitable for visualization of resource consumption

data as it adapts effortlessly to a [floor, room] spatial

layout. A heat map visualizing consumption data

from a multistory building is shown in Figure 1.

Figure 2: AffinityHeat visualization of building

consumption data.

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Represented in 2D a heat map is capable of showing

a complete overview of resource consumption data

across floors and rooms in a large building.

However, as the spatial layout of the real building is

converted to what is basically an ordinal 2D

coordinate system, the affinity between the

visualization and the real building is lowered

considerably. Although a complete overview might

be desired in some circumstances its abstraction

away from the real building’s layout introduces a

mental indirection as the user is required to mentally

map a data point in the heat map to an actual

apartment. This is depicted as AffinityHeat in Figure

2, with the same data and ordinal layout as Figure 1.

For infrequent users an abstract layout can imply

a recurrent comprehension cycle. For users who are

familiar with the actual building and its spatial

layout resource consumption data can be visualized

with considerably higher affinity by complying with

the ordinal layout of floors and rooms in three

dimensions instead of just two dimensions. This

means that an important property of the real building

is retained in the visualization, namely that data

points wrap the same way apartments do in the real

building. This means that just like one would expect,

on, e.g. a floor with 14 apartments, that apartment 2

and 3 are next to each other, so will apartment 14

and 1 be neighbors. In a cuboid building this will

conceal three of the six surfaces, but by making it

rotatable all surfaces can be viewed, though not at

the same time. The issue of lacking overview is

lessened by the heat map’s usage of color intensity

to visualize data, because outliers and patterns will

still be conspicuous. Only now, outliers or patterns

that are a product of their surface will become easier

to identify, such as whether surfaces with high solar

radiation has lower heat consumption.

By visualizing data on a rectangular cuboid

building structure, patterns in data points grouped by

surfaces of the building become more apparent, and

data points wrap the visualization in a manner true to

the real building. Thus, by sacrificing complete

overview, it is possible to create a direction relation

to the spatial layout of the real building, while also

retaining the visual properties of the heat map and

lower the cognitive load on the user.

3.2.2 From Area Map to AffinityArea

The area map utilizes its other visual dimension –

the 2D area of its data. This is appropriate in high

granularity heat maps, but for low granularity heat

maps, with relatively few data points, substituting

color or color intensity with area size frees up color

Figure 3: Area map of building consumption data. Created

with Tableau (Tableau Software, 2014).

as visual dimension to encode other properties of a

dataset. This is done in area map. An area map

version of a heat map is shown in Figure 3.

As in a heat map, outliers are easy to detect in an

area map because a considerably large or small areas

are conspicuous compared to similarly sized areas.

Comparison of two resembling areas, however,

becomes more difficult because a data value is

encoded as area, which is the product of two lengths

multiplied, meaning that two spatial dimensions

must be compared concurrently. Nevertheless,

freeing up color means that this visual dimension

can be used to encode surfaces of a large building by

grouping data using a distinct tone for each surface.

Figure 4: AffinityArea visualization of building

consumption data.

Although individual surfaces becomes

distinguishable a 2D representation of the area map

otherwise share similar drawbacks and advantages

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as the 2D heat map; it adheres to the ordinal layout

of floors and rooms but introduces an abstract layout

in order to facilitate a complete overview of all data

points. Also like the heat map, the area map can be

visualized on a rectangular cuboid achieving high

affinity with the real building in terms of layout as

well as a layout of data points that wraps in a

manner true to the real building. This is visualized as

an AffinityArea visualization shown in Figure 4.

Here the color encoding from the 2D area has been

retained for consistency, but color can be used to

encode other data as area encodes consumption.

3.2.3 From Bar Charts to AffinityBar

Opposed to the previously discussed area map,

which uses area of data points to encode data values,

the bar chart uses area as a supplementary visual

encoding to its primary encoding – extend of bars.

Figure 5: 3D Bar chart of building consumption data.

Created with Microsoft Excel (Microsoft, 2014).

It does this by fixing one dimension of the area

of all bars changing only one dimension in order to

facilitate easy comparison of two bars. The bar

chart, however, adapts poorly to a multidimensional

layout because its layout only expands in a single

direction necessitating either multiple bar charts

with a similar layout or a recurring bar chart to

represent the [floor, room] layout of a large building.

The 3D bar chart in Figure 5 attempts to

facilitate an ordinal 2D layout, similar to the heat

map and the area map but uses height of bars for

encoding data, which both sacrifices a complete

overview of data points as well as potentially hiding

outliers in the lower range of data. Although the 3D

bar chart seems inferior, the heat map and the area

map in terms of its ability to represent layout and

encode data values, the principle of a volume based

bar chart is very useful when combined with a high

affinity spatial layout of a real building. By applying

the principle of a volume based bar chart to the

spatial layout of a real building encoded as a

rectangular cuboid, by fixing two dimension of each

apartment data point and extruding each apartment

in a single direction dependent on its orientation

relative to its position on the building. The result is

the AffinityBar design in Figure 6. The volume of a

data point is used to encode consumption and color

of units is retained for consistency. The volume of

the core structure can be used to encode resource

consumption that is not attributable to an apartment,

and thus serve as a common reference for the extent

of the extrusion of individual apartments. Because

the core structure differ in three dimensions, it can

be difficult to compare it to individual units because

they only expand in one dimension.

Figure 6: AffinityBar visualization of building

consumption data.

However, it does make it possible to derive

whether the apartments’ consumption is

comparatively large or small compared to the

common consumption by evaluating the extend of

the apartments’ extrusion. If the extrusion generally

has a long extend, then the common consumption is

low and if the extrusion generally has a short extend

then the common consumption is high. This function

is not easy to incorporate in the discussed

visualizations because a high common consumption

will drown out the size scale of either color or area.

4 QUALITIES OF AffinityViz

The basic construction is a simplified isometric 3D

model that utilizing a real building’s spatial layout

as layout in order to create a direct relation to that

building. In this section we discuss the key features

of AffinityViz – both common and unique features

for the designs (Figure 2, Figure 4, Figure 6).

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4.1 Simplified 3D Model

AffinityViz’s usage of volume in a visual

representation is new in that it uses volume to

achieve physical affinity with the building whose

data is visualized. All three AffinityViz designs uses

the three dimensions of units of a multistory

building as the key layout feature. In the AffinityBar

(Figure 6) visualization, the volume created by the

three dimensions of units of a multistory building

are used as a relative measure for extent of the single

units. This is done by using common consumption

(e.g. elevator electricity usage) as reference for

calculating the volume (extent) of a single unit. If no

common consumption is available or it is not

appropriate to use, then the volume of a cubic unit is

set to the average consumption of all units.

AffinityViz is designed to achieve physical

affinity by mimicking the spatial layout of a

building, boiled down to its simplest rendition

retaining a common unit (apartment, office, etc.)

used for measurement. This enables AffinityViz to

retain an important spatial relationship between units

– namely that sequence of units is both sequential

and it wraps from highest to lowest. Meaning that

e.g. apartment 14 and 1 on a given floor are situated

next to each following the same rules that situate e.g.

apartment 3 and 4 next to each other.

For all three AffinityViz designs an issue arises

with corner units, which are located on a single

surface. It requires consideration from

implementation to implementation on which surface

to place it, and thus potentially hinders immediate

generalizability. Units that are only somewhat

similar such as offices combined to create a single

larger office, can be handled to some extent by

aggregating multiple units into a single larger

composite units. The orientation of the individual

unit, i.e. the surface on which the unit is situated,

reflect the orientation of the corresponding

apartment or office. Furthermore, the orientation of a

unit on the spatial layout helps to group units

directly related to surfaces of a real building as well

as distinguish between such groups because, a unit

appear distinctively different due to the isometric

perspective. The orientation is most pivotal in the

AffinityBar (Figure 6) design as a unit bar expands

and contracts along a single dimension only. In the

AffinityBar design, both the units’ data and the

common data is encoded with volume, but in

different ways. Where the spatial layout expands

into three dimensions, the volume of a single unit

always fixes two dimensions, and data expanding in

a single dimension. This can make units stand out it,

but it can also potentially hide units with low

extrusion, necessitating rotation to detect such units.

4.2 Low Cognitive Load

AffinityViz exhibits its true strength in the low

cognitive load it introduces to the user when

compared to the abstract layout of generic types of

visualizations. This is to a large degree owing to

properties elaborated in the previous three

subsections – the 3D layout, the simplified model,

and the 1D volume growth. Together, these three

properties establish a visualization representation

that has a high affinity with the real building, thus

using the real building as a direct frame of reference

because the visualization shares the same basic

structure as the real building.

The overload of three visual dimensions for both

layout and data representation lessens the need for

legends or labels describing the location of single

apartments or offices as often needed in generic

types of visualizations. This means that the user does

not need keep an ongoing reference to an abstract

coordinate system in order to place a unit in its

spatial context. Furthermore, as described

previously, the spatial layout of AffinityViz wraps

around the building in the same way as the

apartments or offices do around the real building.

This means that adjacent units in the real building

also are adjacent in AffinityViz’s layout. This makes

AffinityViz suitable for users with different

prerequisites on the analysis.

What AffinityViz does not support effectively,

as discussed in 3.2, is a complete overview of units

because units on two faces of the AffinityViz will be

hidden from view. This means that some analyses,

such as comparing units on opposite surfaces, will in

fact introduce a higher cognitive load because a user

will need to remember non-visible units.

Although AffinityViz is already contextualized

through its design as a simplified model of a real

building, more contextual information can be added

to create an even stronger relation to a real building.

For instance a compass can indicate the building’s

orientation relative to the corners of the world. Other

contextual enhancements could be to show solar

radiation to assist in analyzing differences in heat

and electricity consumption between surfaces with

differing solar radiation. Another enhancement

could be to add simple landmarks or infrastructure

elements such as adjacent roads or structures.

4.3 Visual Analysis

The 3D layout of AffinityViz provide a natural

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segmentation of units into groups that adhere to the

real buildings structure thus the overview of the

location of units in the building is a part of

AffinityViz. This assists in analyzing patterns in

consumption either on entire sides of a building,

between different sides. Distilling apartments or

offices into uniform units, is essential in AffinityViz

to compare apartments or offices. The low-fidelity

of AffinityViz features single units to convey their

corresponding data because of the underlying

uniformity of units, as distortion of similar units

expresses variations in the underlying powerfully.

For the AffinityBar (Figure 6) design, it is only a

single dimension of a unit that is used as a measure

for visually comparing units. This makes it easy to

spot high outliers, or the lack thereof, than if units

were transformed in two or three dimensions

dependent based on the data. The differing

orientation, and dimension of growth, of a single

unit can cause visual indirection because units

adjacent to each other can grow in different

directions.

Currently AffinityViz is implemented with

horizontal rotation and mouseover tooltips as the

only interaction, but all three designs will benefit

greatly from rich interaction to support users to

create and rapidly test and rethink hypotheses. All

three AffinityViz designs can be utilized to visualize

flow of live data of replay of historic (user

controlled or not) by animating the data points over

time, though it would be most distinct in the

AffinityArea (Figure 4) and AffinityBar designs.

This is easily done in our current AffinityViz

implementation because employed SVG elements

are all animatable.

5 FROM DATA TO AffinityViz

AffinityViz was developed experimentally for a 12

story apartment building in Aarhus, Denmark.

5.1 Data Management

All data from the building is transmitted to and

stored using the Karibu architecture (Christensen, et.

al., 2014), from where it is retrieved and massaged

into a format appropriate for loading in a client web

browser and rendering with SVG elements.

Although the current implementation of AffinityViz

only visualizes electricity data from the building, it

is interchangeable with other data sources extracted

from the Karibu architecture, such as consumption

of district heating, hot/cold water, CO

, etc. This

will be subject to future work on AffinityViz

implementations as it matures into a more complete

tool for visual analysis.

5.2 Browser-based Visualization

AffinityViz is implemented using JavaScript to

create SVG elements in a browser DOM, which in

return renders the elements. As SVG elements are

2D, and therefore has no real concept of depth, there

are obstacles in creating a 3D visualization, as

angles and lengths of all shapes need to be

calculated manually. Although this therefore might

seem like a counterintuitive choice, opposed to e.g.

WebGL or standalone 3D modelling software,

rendering AffinityViz using SVGs enables us to take

advantage of the rich set of interactivity supported

by browsers. E.g. transitioning the extent of a single

unit becomes trivial, as many SVG elements are

animatable. Furthermore, the vast amount of existing

JavaScript libraries that operate on DOM elements

can be applied, and therefore this implementation of

AffinityViz becomes open for further development.

5.3 AffinityViz in Use

We have collaborated with facility managers and

conducted tests with the three versions of

AffinityViz from section 3.2. This has lead us to

identify central elements of the AffinityViz designs.

Overview of data represented in AffinityViz is

generally lower when compared to 2D counterparts

as two sides always are hidden from sight. But

AffinityBar handles this better than the AffinityHeat

and AffinityArea designs because extreme high

outliers are visible even if their corresponding

surface is not front-facing, as they extent greatly

from the building’s core. However, bars with little

extent can potentially be hidden from view by

neighboring bars with larger extent. We can alleviate

this by implementing full horizontal rotation,

because it enabled users to eventually view all bars.

But this still is a drawback compared to the 2D

visualizations discussed in section 3.2. Because the

layout of the three AffinityViz designs provides

spatial reference to the building, it supports queries

based on spatial position of units. This is due to units

are both grouped onto surfaces and has a significant

spatial location, reflecting their real location.

6 PROSPECTS OF AffinityViz

AffinityViz has both advantages and shortcomings,

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i.e. the visualization technique that do not suit all

potentially use scenarios equally well.

Currently, AffinityViz relies on a simple

recurring floor plan of a rectangular circumference

of units multiplied by a number of floors. Although

many apartments and office buildings have such a

layout, AffinityViz’s generalizability is conditioned

since buildings with a more complex layouts may

not be suited for having consumption data visualized

using AffinityViz techniques. Furthermore, because

AffinityViz is 3D it is only suitable for visualizing

buildings where units are in the circumference of the

building.

Because the current implantation of AffinityViz

the 3D model is isometric, meaning that it is a

construction of parallelograms and thus has no

vanishing points, it can be argued to violate Tufte’s

Lie Factor (Tufte, 1983) because similar sized units

will be perceived as not similarly sized due the

perceived perspective of the visualization. Although

this can obfuscate precise comparison of far apart

units it does not hinder holistic exploratory analysis.

In section 3.2. we discussed differences in layout

properties the AffinityViz designs and the

conventional visualization techniques. Together with

the inferior overview of data in the AffinityViz

designs, as documented in the evaluation, this

illustrates that AffinityViz will not fully replace

related conventional visualization techniques in all

cases. Rather, it is a novel concept for visualizing

consumption data from buildings while retaining a

building’s spatial layout, thus lowering users’

cognitive load.

Next steps will be to mature the AffinityViz

visualization technique with a more advances set of

interactions, e.g. filtering of data and open access to

data sets. Such features can make it a tool usable a

wider range of professions. Also including users

with non-technical backgrounds, who have a desire

to analyze data, but not necessarily has prerequisites

for using conventional visualization tools. This will

be developed through continued professional

consultation with facility managers and experts from

other professions who are relevant to include.

7 CONCLUSIONS

This paper has introduced AffinityViz techniques for

making generalizable and higly affine visualizations

of consumption data from multistory buildings.

Three AffinityViz designs were implemented and

evaluated with expert users from the facility

management domain. The evaluations showed that

the AffinityBar technique is slightly better than the

AffinityHeat and AffinityArea techniques with

respect to minimizing the cognitive load when users

have to deal with different visual analytics tasks that

requires mapping of results to locations in buildings.

The implementation of the AffinityViz data supply

chain has been described for tall multistory

buildings. However, the techniques can be tailored

to work for most archetype building layouts of office

buildings, schools, and factories. The techniques are

under continual development with the goal of

generalizing to cover more building types and

supporting AffinityViz visualizations to integrate a

wide range of real world data.

ACKNOWLEDGEMENTS

The work was supported by Danish DSF grant no.

11-115331. We wish to thank our colleagues in the

EcoSense project for their contributions.

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