AN OPEN-SOURCE PROGRAM TO ANIMATE AND VISUALIZE THE

RECORDED TEMPERATURE AND RELATIVE HUMIDITY DATA FROM

DATALOGGERS INCLUDING THE BUILDINGS 3D GEOMETRY

by

Tareq Ali Baker

A Thesis Presented to the FACULTY OF THE SCHOOL OF ARCHITECTURE

UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements of the Degree

MASTERS OF BUILDING SCIENCE

August 2007

Copyright 2007 Tareq Baker

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Dedication

I dedicate this thesis to my family in Palestine and my friends who showed me so much

love and support over the years.

iii

Acknowledgments

This thesis could not have been possible without the generous contributions of many

organization and individuals.

United States Agency for International Development (USAID) for giving me a full

scholarship to continue my graduate studies in the US

The Academy for Educational Development (AED) for managing my scholarship

program

My thesis committee for their continuous support, advice and encouragement

Prof. Marc Schiler for spending many hours working helping developing and organizing my ideas

Prof. Karen Kensek for her efforts and valuable inputs which significantly enhanced my thesis output

Prof. Murray Milne for his advice which helped me to better understand how to conduct

my research.

Special thanks to Prof. Douglas Noble for helping me organize my ideas during the early time of my

research and through his valuable inputs during my thesis presentations.

Prof. Thomas Spiegelhalter who gave me many ideas to enhance my thesis research

My MBS classmates for their help, support and love

http://rds.yahoo.com/_ylt=A0oGklgLOHRGN.UAUfNXNyoA;_ylu=X3oDMTE2Z21mMTgwBHNlYwNzcgRwb3MDMQRjb2xvA3cEdnRpZANERlI1Xzg1BGwDV1Mx/SIG=1193canhi/EXP=1182108043/**http%3a/www.usaid.gov/http://rds.yahoo.com/_ylt=A0oGkwTPOHRGWw4A4CdXNyoA;_ylu=X3oDMTE2dDIxbGdtBHNlYwNzcgRwb3MDMgRjb2xvA3cEdnRpZANERlI1Xzg1BGwDV1Mx/SIG=11fnrt6sg/EXP=1182108239/**http%3a/www.aed.org/HIVAIDS/

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Table of Contents

Dedication ........................................................................................................................ ii

Acknowledgments........................................................................................................... iii

List of Figures ............................................................................................................... viii

Abstract ........................................................................................................................ xvii

Hypothesis.........................................................................................................................1

Introduction.......................................................................................................................2

Part I: Background ............................................................................................................6

CHAPTER 1: Building Thermal Comfort ........................................................................6 1.1. Introduction............................................................................................................6 1.2. Building Thermal Performance..............................................................................7 1.3. Thermal Comfort ...................................................................................................7

1.3.1. Environmental Variables ................................................................................8 1.3.2. Personal Variables ........................................................................................11

1.4. Comfort Zone.......................................................................................................12 1.5. Bioclimatic Charts ...............................................................................................13

1.5.1. Psychrometric Chart......................................................................................14 1.5.2. Olgyay Chart .................................................................................................16

1.6. Chapter Summary ................................................................................................18

CHAPTER 2: Dataloggers..............................................................................................20 2.1. Introduction..........................................................................................................20 2.2. What is a Datalogger............................................................................................20 2.3. Datalogger applications .......................................................................................21 2.4. The Main Selection Criteria for Dataloggers.......................................................22

2.4.1. Input Signal ...................................................................................................22 2.4.2. Number of Input Channels............................................................................22 2.4.3. Sample Rate and Memory Capacity .............................................................22 2.4.4. Size................................................................................................................23

2.5. How a Datalogger is used ....................................................................................23 2.6. Datalogger Proprietary Programs ........................................................................24 2.7. Datalogger Vs Data Acquisition Systems............................................................25 2.8. Using Dataloggers in Building Research. Examples from Previous Published Researches..................................................................................................26

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2.8.1. Thermal Comfort in a Sustainable House by Frank Lloyd Wright by Marc Schiler and Sumit Brahmbhatt.......................................................................27 2.8.2. Energetic analysis of a passive solar design, incorporated in a courtyard after refurbishment, using an innovative cover component based in a sawtooth roof concept by M.R. Herasa, and others............................................30

2.9. Chapter Summary ................................................................................................34

CHAPTER 3: Information Visualization........................................................................35 3.1. Introduction..........................................................................................................35 3.2. What is Visualization...........................................................................................35 3.3. Why Visualization ...............................................................................................35 3.4. The Visualization Process....................................................................................37 3.5. What Excellent Visualization Should Display.....................................................38 3.6. Memory, Cognitive System and Visual Perception.............................................38 3.7. Graphical Objects Design (the Glyphs) ...............................................................39 3.8. Chapter Summary ................................................................................................43

CHAPTER 4: Reviewing Existing Programs .................................................................44 4.1. Introduction..........................................................................................................44 4.2. Proprietary Programs ...........................................................................................45

4.2.1. General Description ......................................................................................45 Table 1: Comparison between the capabilities of three propriety programs .................................................................................................................46 4.2.2. BoxCar version 4.0 by ONSET ....................................................................47 4.2.3. TrendReader 2 Standard by ACRSYSTEMS ...............................................49 4.2.4. MicroLabPLUS Fourier Systems Ltd ...........................................................54

4.3. Other Programs ....................................................................................................57 4.3.1. Programs focusing on Interpreting and Visualizing Tabular Data ...............57 4.3.2. Programs Used to Visualize a Buildings thermal Performance. ................59

4.4. Chapter Summary ................................................................................................63

Part II: Research..............................................................................................................65

CHAPTER 5: The 3DDataScene Program .....................................................................65 5.1. Introduction..........................................................................................................65 5.2. The 3DDataScene Process ...................................................................................65 5.3. The Program Interface .........................................................................................67 5.4. Quick Example of Using the Program.................................................................68 5.5. The Program Main Classes ..................................................................................73 5.6. The Program Methods and Tools.........................................................................77

5.6.1. The Data Visualization Methods ..................................................................77 5.6.2. Importing the Data text file...........................................................................79

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5.6.3. The Datalogger Options ................................................................................81 5.6.4. Importing the Building Model from a DXF file ...........................................82 5.6.5. Data Navigation ............................................................................................83 5.6.6. Averaging the Data by Hours, Days and Months .........................................83 5.6.7. The help Tool ................................................................................................84

5.7. The Programs Flow Diagram .............................................................................86 5.8. The Programming Language and Libraries .........................................................86 5.9. Chapter Summary ................................................................................................87

CHAPTER 6: Using the 3DDataScene Program............................................................88 6.1. Introduction..........................................................................................................88 6.2. Starting 3DDataScene Program ...........................................................................88 6.3. New File Defaults ................................................................................................89 6.4. Preparing and Importing the Building Model from a DXF File ..........................90 6.5. Navigating the Project in 3D................................................................................96 6.6. Navigating the Project by Level ..........................................................................98 6.7. Adding Datalogger to a Level............................................................................100 6.8. Navigating and Animating the Data ..................................................................107 6.9. Averaging the Data ............................................................................................109 6.10. The Graph-View ..............................................................................................110

6.10.1. The Graph View Drop Down Menu .........................................................113 6.11. Changing the Datalogger Options....................................................................114 6.12. The All Levels Node Dropdown Menu ...........................................................116 6.13. A Level Node Drop down Menu .....................................................................119 6.14. A Datalogger Node Dropdown Menu..............................................................121 6.15. Adding more Dataloggers and Levels..............................................................125 6.16. Chapter Summary ............................................................................................130

CHAPTER 7: Conclusion .............................................................................................131

CHAPTER 8: Future Works .........................................................................................132 8.1. Adding Analysis Tools ......................................................................................132

8.1.1. Adding some Design Strategies Advice by Using the Psychrometric Chart ...............................................................................................................133 8.1.2. Thermal Comfort Analysis Tool .................................................................134 8.1.3. Queries Tool................................................................................................134 8.1.4. Statistics Summary......................................................................................134 8.1.5. Math Equations ...........................................................................................134 8.1.6. Data Interpolation .......................................................................................135 8.1.7. Curve Fit .....................................................................................................135

8.2. Improving the Program Interface.......................................................................136 8.2.1. Drawing Aids ..............................................................................................136

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8.2.2. Project Units Setup .....................................................................................136 8.2.3. Improving the Geometry View ...................................................................137 8.2.4. More Editing Features.................................................................................137 8.2.5. Annotating and Highlighting Features........................................................137 8.2.6. Improving the Graph View .........................................................................138 8.2.7. Adding Save Movie to File .........................................................................138 8.2.8. Improving the DXF Importing Tool ...........................................................139 8.2.9. Importing the Dataloggers Locations from a DXF file...............................139 8.2.10. Support Importing the Building 3D Geometry in Different Formats .......140 8.2.11. Redo and Undo Functions.........................................................................140 8.2.12. Text File Importing Template ...................................................................140 8.2.13. Override the Dataloggers Visualization Options ......................................141 8.2.14. Docking Panels and Multiple Geometry-View Windows ........................141

8.3. Improving the Visualization ..............................................................................142 8.3.1. Sun Diagram ...............................................................................................142 8.3.2. Importing Output files from Building Energy Simulation Programs .........143 8.3.3. Other Graphs Type......................................................................................143

8.4. Chapter Summary ..............................................................................................144

Bibliography .................................................................................................................145

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List of Figures

Fig. 0-1: The Freeman House lower floor plan. The small circles are the locations of the dataloggers used .........................................................................................................3

Fig. 0-2: Example of the recorded data from the Freeman House presented in time series graphs and Olgyay charts........................................................................................3

Fig. 0-3: Front and back view of ONSETs HOBO datalogger........................................4

Fig. 1-1: Heat exchange between man and surrounding .................................................9

Fig. 1-2: The main variables related to thermal comfort. ...............................................10

Fig. 1-3: Relation between Effective Temperature and percentage observations indicating comfort. ..........................................................................................................13

Fig. 1-4: Schematic drawing of the Psychrometric chart...............................................15

Fig. 1-5: Psychrometric chart with winter and summer comfort zones.........................15

Fig. 1-6: Olgyay bioclimatic chart, for the U.S moderate zone inhabitation.................17

Fig. 1-7: Olgyay bioclimatic chart, Schematic bioclimatic index .................................18

Fig. 2-1: Front and back view of the ONSETs HOBO datalogger...............................21

Fig. 2-2 : Example of datalogger readings in delimited text format...............................24

Fig. 2-3: Upper floor plan of the house with indication to the locations of the used dataloggers ......................................................................................................................27

Fig. 2-4: Lower floor plan of the house with indication to the locations of the used dataloggers. .....................................................................................................................28

Fig. 2-5: The used devices to monitor the house . .......................................................28

Fig. 2-6: Example of the recorded data presented in time series graph and Olgyay chart.................................................................................................................................29

Fig. 2-7: (a) Global view of space conditioned. (b) Global view of building.................31

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Fig. 2-8: Scheme of numbering and location of the vertical lines of measurement into the patio ...................................................................................................................32

Fig. 2-9: Sensors locations scheme . ..............................................................................32

Fig. 2-10: (a) Level 14th February, (b) Level 114th August, and (c) Level 125th June .................................................................................................................................33

Fig. 3-1: Passamoquoddy Bay visualization. (The Canadian Hydrographic Service)....36

Fig. 3-2: A schematic diagram of the visualization process ...........................................37

Fig. 3-3: When integrating multiple data attributes into a single glyph, more information can be retained in the working memory......................................................40

Fig. 3-4: The display dimensions pairs ranked in order from highly integral to highly separable ..............................................................................................................41

Fig. 3-5: Example of glyphs designed according to two display attributes. Top are more integral pairs and bottom are more separable pairs ...............................................41

Fig. 4-1: BoxCar can open multiple graph views. it can also show multiple data files the same graph (right graph) ...........................................................................................48

Fig. 4-2: BocCar table view (Left side) . the program can display logger info and details like launch parameters (left upper side). ...........................................................49

Fig. 4-3: TrendReader program main interface. .............................................................51

Fig. 4-4: Graph View and Graph Statistics Table...........................................................52

Fig. 4-5: Table view........................................................................................................52

Fig. 4-6: Equation Wizard...............................................................................................53

Fig. 4-7: The Realtime Window .....................................................................................53

Fig. 4-8: In addition to multiple tables and graphs view the program has a multiple meters view .....................................................................................................................55

Fig. 4-9: Cradle View, the user can overlie the logger icon (green icon) according to its location in the image. .................................................................................................56

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Fig. 4-10: Cradle view. The icon of a logger turn read instead of green if the logger exceeds the alarm level ...................................................................................................56

Fig. 4-11: Example of using Microsoft Excel to visualize dataloggers measurements. (1) Time series vs. temperature graph. (2) Bioclimatic Olgyay graph. ..............................................................................................................................58

Fig. 4-12: Example of using ArcScene to visualize data in 3D......................................59

Fig. 4-13:The hourly internal temperature, outside temperature, beam solar, diffuse solar and wind speed profiles of zone 1 in January 1st , displayed in graph view. .......61

Fig. 4-14: The zones temperature in May 27th at 2:30 pm. each zone is colored according to its temperature value. The legend to the left shows the temperature ranges color. ....................................................................................................................61

Fig. 4-15: The bioclimatic Psychrometric Chart for Los Angles between January 1st to December 31st. ...........................................................................................................62

Fig. 4-16: The weekly average temperature summary of Los Angles...........................63

Fig. 5-1: The 3DDatScene program process...................................................................66

Fig. 5-2: The 3DDataScene program interface. ..............................................................67

Fig. 5-3: The user can import DXF file from the file menu ...........................................69

Fig. 5-4: The imported geometry ....................................................................................69

Fig. 5-5: The user right click on Level-1 and select the add datalogger command........70

Fig. 5-6: The user locates the datalogger inside the Geometry-View.............................70

Fig. 5-7: Importing the text file steps: First the user selects the data text file. Second the user selects the temperature and relative humidity fields. Third the user adds the datalogger to the project..................................................................................................71

Fig. 5-8: The datalogger are distinguished in the Graph, Outline, and Geometry view by the selected color...............................................................................................72

Fig. 5-9: the user can navigate the data either by clicking inside the Graph View or by playing animation.......................................................................................................73

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Fig. 5-10: The left image is the whole building geometry without clipping (all levels) . The right image is One Level and its dataloggers. The level geometry was clipped out from the whole building geometry so the user can only navigate this level.................................................................................................................................74

Fig. 5-11: When the user right click on a level node, a drop menu shows the options available to mange the level............................................................................................74

Fig. 5-12: When user right click on the all-levels node, a drop menu shows the options available to mange the all-levels node. ..............................................................75

Fig. 5-13: Left image: the clipping volume objects and its planes. Right image: the tool the user uses to control the coordinates of the clipping volume..............................76

Fig. 5-14: The Datalogger bar and thin-strip geometries visualized according to the groups classification method. ........................................................................................79

Fig. 5-15: The data text file, the top red rectangle highlights the file header which has the names of the data fields. The small rectangles highlight the delimiter character, in this case ,.................................................................................................80

Fig. 5-16: The program text file importing tool which is used to read and convert the text file into a data table. When user right clicks a column header he can select from the drop menu if this column is temperature or relative humidity field.................81

Fig. 5-17: The datalogger options dialog in which the user can change the properties of all dataloggers in the project.......................................................................................82

Fig. 5-18: The recorded plot is a stepped graph..............................................................83

Fig. 5-19: The averaging data tool. The user can define a specific time interval for all dataloggers. And the program averages the data according to this interval. .............84

Fig. 5-20 : The highlighted rectangles show the help icons and the quick help tips box. The user can access help about specific panel by clicking the help icon at the corner of the panel. .........................................................................................................85

Fig. 5-21: 3DDataScene Programs Flow Diagram........................................................86

Fig. 6-1: The 3DDatascen initial interface......................................................................89

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Fig. 6-2: The program by default has one level called (Level-1). Level-1 has a default height of 10 units and its clipping volume has the dimensions highlighted by the red arrows..................................................................................................................89

Fig. 6-3: A message box will prompt to warn the user that he has to follow specific steps in order to successfully import the DXF file .........................................................91

Fig. 6-4: The building 3D model was drawn inside AutoCAD 2006. The drawing has many layers...............................................................................................................92

Fig. 6-5: The building model has two levels; each level has a 10 ft height. The first level has an elevation of zero and the second level has an elevation of 12ft..................92

Fig. 6-6: The user should write in the AutoCAD command line 3Dsout, then he selects the objects to export and a save to 3Ds dialog will appear..............................93

Fig. 6-7: The user must open a new drawing and write 3Dsin in the command line , an Import 3Ds file dialog will appear. The user will select the same file exported in the previous step. ........................................................................................................95

Fig. 6-8: After importing the 3Ds file into AutoCAD, the building geometry will be built from multiple 3Dfaces. ...........................................................................................95

Fig. 6-9: The building geometry imported inside the 3DDatascene. Since Level-1 is the active level, only the geometry parts inside the clipping volume of Level1 are shown. .............................................................................................................................96

Fig. 6-10: The Geometry-View navigation commands. The user can keep track of the active view and level from the text located below the main toolbar (the highlight red box) ...........................................................................................................................96

Fig. 6-11: the rotate dialog..............................................................................................97

Fig. 6-12: The user can pan the view by clicking the rectangular icons; the highlighted red rectangles; located at the middle of each edge of the Geometry-View panel. .....................................................................................................................98

Fig. 6-13: Switching to specific level view clipping volume. In this example is Level-1. ...........................................................................................................................99

Fig. 6-14: By selecting the All-levels node and right click, a drop down menu will appear. The user can select to view all levels in the project by clicking View All Levels. .........................................................................................................................100

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Fig. 6-15: All Levels view and its clipping volume......................................................100

Fig. 6-16: When the user adds a datalogger to a level the Geometry-View will switch to the top view of this levels clipping volume. The user moves the mouse over the view and click at the target location. ..............................................................103

Fig. 6-17: When the user click inside the view, the Import Datalogger File dialog will appear.....................................................................................................................103

Fig. 6-18: The user will select the data text file correspondent to this datalogger. Just for illustration, this example shows the text file previewed in the NotePad program. ........................................................................................................................104

Fig. 6-19: When the program finished reading and converting the text file, all columns available in the file will be listed in the table view. The Error in reading the file is listed in the lower part of the dialog. ............................................................104

Fig. 6-20: The user must select which columns are having the temperature and relative humidity data by clicking over a column header and right click to select from the drop down menu if this column has the temperature or relative humidity data................................................................................................................................105

Fig. 6-21: Specifying the datalogger elevation above the parent level.........................105

Fig. 6-22: The presentation of the datalogger cross symbol inside the Geometry-View..............................................................................................................................106

Fig. 6-23: The program will only keep three columns and will prompt the user to select a color for this datalogger. ..................................................................................106

Fig. 6-24: When the user adds a datalogger and selects its color; in this example orange; this color will be assigned to the dataloggers node in the Project-Outline, the dataloggers cross symbol in the Geometry-View, and a graph curve of this datalogger inside the Graph-View. ...............................................................................107

Fig. 6-25: 1-The level node shows the average of the active dataloggers. 2- The datalogger node shows the values for both temperature and relative humidity of this datalogger. 3- The current date/time. 4- The height and color of the datalogger change to reflect the new value. 5- The label shows the current value. 6- The temporary gray arrow. 7- The selection of the visualization mode. 8- The play and stops buttons. 9-The average datas button. ................................................................109

Fig. 6-26: The interval was set to each 15 minutes.......................................................110

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Fig. 6-27: The interval was set to each 6 hours. The stepped graph curve shows the new interval setup. ........................................................................................................110

Fig. 6-28: In Single-Mode, the Graph-View shows the active dataloggers of the viewed level which in this case level-1. The curves present the temperature values. The title of the graph is set to temperature. ..................................................................112

Fig. 6-29: In Single-Mode, the Graph-View shows the active dataloggers of the viewed level which in this case level-1. The curves present the relative humidity values. The title of the graph is set to relative humidity. .............................................112

Fig. 6-30: In Combined-Mode, the Graph-View shows the active dataloggers of the viewed level which in this case level-1. The solid lines present the relative humidity values because the relative humiditys radio button is selected. The dash lines present the temperature values. The title of the graph is set to relative humidity and temperature. ..................................................................................................................113

Fig. 6-31: The Graph-View dropdown menu. ..............................................................114

Fig. 6-32: When selecting this option, all dataloggers in the project will be located at specific height from the parent level in this example 5. ...........................................115

Fig. 6-33: When selecting this option, each datalogger in the project will be located at its elevation (showed by a cross symbol) above the parent. .....................................116

Fig. 6-34: The All-Levels node drop down menu.........................................................117

Fig. 6-35: Add Level Dialog.........................................................................................118

Fig. 6-36: the Clip Plane Options dialog of the All Levels node..................................118

Fig. 6-37: The DXF Geometry Options........................................................................119

Fig. 6-38: A level node drop down menu. ....................................................................120

Fig. 6-39: Single levels Clip Plane Options. ...............................................................121

Fig. 6-40: a level property dialog..................................................................................121

Fig. 6-41: A datalogger node drop down menu. ...........................................................123

Fig. 6-42: A datalogger property dialog. The dialog first tab has the datalogger general properties..........................................................................................................123

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Fig. 6-43: A datalogger property dialog. The dialog second tab has the original data table which was generated from importing the datalogger text file. The time interval between readings is 15 minutes. ...................................................................................124

Fig. 6-44: A datalogger property dialog. The dialog third tab has the averaged data table which was generated from averaging the data in the original data table according to specific time interval, in this example every 6 hours...............................124

Fig. 6-45: When the user moves a datalogger the Geometry View switch to the top view of the parent level of this datalogger. A temporary datalogger will be drawn and moving with the mouse cursor and a line will be drawn from the center of the original dataloggers center and the temporary dataloggers center. ............................125

Fig. 6-46: More Dataloggers were added to Level-1....................................................126

Fig. 6-47: A new level, named Level-2, was added to the project and many dataloggers were added to this project. The visualization mode is set to single mode using the temperature field............................................................................................126

Fig. 6-48: Level 2 dataloggers were visualized by using the combined mode and relative humidity was used as main field. The values of temperature and relative humidity for each datalogger are written beside the datalogger node in the Project Outline. Only the relative humidity values for each datalogger are written beside the datalogger in the Geometry View. The Graph View show both relative humidity (solid line) and temperature (dashed line) . ..................................................................127

Fig. 6-49 : The clipping volume of level-2 was adjusted to remove some parts form the building geometry. ..................................................................................................127

Fig. 6-50: Right view of Level-2 and all dataloggers are located at the same elevation above Level-2................................................................................................128

Fig. 6-51: View of Level-2 and each datalogger is located at its own elevation above Level-2................................................................................................................128

Fig. 6-52: Right view of All Levels , the clipping volume was adjusted to remove some parts from the building. .......................................................................................129

Fig. 6-53: Axon view of All Levels, the clipping volume was adjusted to remove some parts from the building. .......................................................................................129

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Fig. 8-1: The Climate Consultant program uses the psychrometric chart to show the boundaries of the some design strategies to improve the building performance of the buildings........................................................................................................................133

Fig. 8-2: Examples of data interpolations plotted in graphs. ........................................135

Fig. 8-3: Example of linear curve fitting (left graph) and nonlinear curve fitting (right curve) . ................................................................................................................136

Fig. 8-4: An example of snapshots from AutoCAD 2006 of docked and not docked panels, ...........................................................................................................................142

Fig. 8-5: An example of new graphs ideas. ..................................................................144

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Abstract

The development in datalogger technologies and data processing programs has

helped architects and researchers analyze a buildings thermal performance over time.

Nonetheless, most of the existing programs are not specialized for this kind of research.

As a result, most researchers have relied on 2D or 3D graphs to study and present their

data. Such data presentation does not help in the visualization of the spatial distribution

of dataloggers inside the building geometry. Consequently, the absence of the spatial

distribution information may obscure some knowledge about how a buildings layout

may affect its thermal performance. The product of this thesis is an open-source

computer program called 3DDataScene that combines data from the dataloggers and 3D

building geometry.

Key words: 3DDataScene, Visualization, buildings thermal performance, temperature,

relative humidity, Olgyay and Psychrometric chart, datalogger, sensor, open source, 3D

software.

1

Hypothesis

A computer program that uses the building geometry as boundary to visualize

the data aggregated by dataloggers may help architects study the change of temperature,

relative humidity, and other data inside buildings.

2

Introduction

Building thermal performance refers to the heat flow inside buildings. It is

related to a buildings interaction with the surrounding environment. A building

interacts differently with the surrounding environment depending upon its orientation,

shape, envelopes materials, window size and location, building shading, and natural

ventilation. This interaction affects the indoor environment, which has a direct effect on

the thermal comfort of the building occupants. Consequently this effect has great

implications on the amount of energy required to cool, heat, and ventilate the building

to maintain a pleasant comfort level.

Studying the building thermal performance of existing buildings has attracted

many researchers and architecture students. For example Schiler and Brahmbhatt

studied the aspects of thermal comfort and sustainable design in the Freeman House,

designed by Frank Lloyd Wright, by using dataloggers to record the temperature and

relative humidity from the house as shown in Fig. 0-1 and Fig. 0-2.

Fig. 0-1: The Freeman House lower floor plan. The small circles are the locations of the dataloggers used (source: Schiler, Brahmbhatt, 2006).

Fig. 0-2: Example of the recorded data from the Freeman House presented in time series graphs and Olgyay charts (source: Schiler, Brahmbhatt, 2006).

In general architects or researchers study building performance by installing

dataloggers inside a building or multiple buildings to record the change in the indoor

environment over a specific period of time. A datalogger (data logger) is an electronic

device that records data over time. Usually a datalogger is battery powered and has a

3

microprocessor. A datalogger reads the measurements by using sensors and saves the

data on its internal memory as shown in Fig. 0-3.

Fig. 0-3: Front and back view of ONSETs HOBO datalogger (source: http://www.1800loggers.com/support/manual_pdfs/loggers/2869_J_MAN_HO8_03x_08.pdf).

Typically, a datalogger is shipped with a proprietary program suited for it. The

proprietary program is used to launch and set up the datalogger, specifying

measurement interval, frequency, alarm settings, and start time and to subsequently

download the recorded data from the dataloggers internal memory to computer. In

general, proprietary programs allow users to plot the data in 2D XY graphs. More

sophisticated programs can plot data in 3D graphs. In addition, proprietary programs

allow their users to export the recorded data to a text file or spread sheet. The user can

use these files to analyze and visualize the data by using other programs such as

Microsoft Excel or GIS packages.

Visualization is an important process in transforming data into a readable form.

Information visualization summarizes data in a way that communicates with our natural

senses and vision to help us in understanding information. There are many existing

programs that can be used to visualize dataloggers data. Through this thesis research, a

4

http://www.1800loggers.com/support/manual_pdfs/loggers/2869_J_MAN_HO8_03x_08.pdf

5

review of the existing proprietary programs and other visualization programs was done.

It was found that these programs do not visualize the data inside the building in relation

to their location within the building.

This thesis was conducted to develop a computer program to visualize this data

by including the buildings 3D geometry. The program was developed by using

Microsoft Visual Studio 2005. Two open-source libraries were used: OpenGl for 3D

visualization and Zedgraph for plotting data in graph view.

6

Part I: Background

CHAPTER 1: Building Thermal Comfort

1.1. Introduction

The intention of this research was to develop a program to visualize the recorded

measurements via dataloggers in relation to a buildings 3D form. The program is

expected to help architects and researchers to study a buildings thermal performance

over time.

This chapter briefly introduces the terms buildings thermal performance and

human thermal comfort. Moreover, this chapter presents the bioclimatic charts that

are widely used to visualize and analyze the environmental characteristics of a given

location in relation to human thermal comfort.

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1.2. Building Thermal Performance

Building thermal performance refers to the heat flow inside buildings. Its related to

the building interaction with the surrounding environment. Basically, this interaction

has four forms (Givoni, 1998, p 49):

Solar exposure of the glazed and opaque elements of the buildings envelope (its

walls and roof);

Solar heat gain;

Conductive and convective heat gain from, or loss to, the ambient air;

Natural ventilation and passive cooling.

Buildings interact differently with the surrounding environment. However, in

general the main elements which affect this interaction are: building orientation, shape,

envelope materials, window size and location, building shading, and natural ventilation

(Givoni, 1998, p 49).

1.3. Thermal Comfort

ASHRAE Standard 55-2004 defines thermal comfort as that condition of mind

which expresses satisfaction with thermal environment.

To be thermally comfortable a person must not feel either too hot or too cold. The

physiological principle for this comfort feeling is that the amount of heat produced by

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the body is balanced with the heat loss, comfortably within the bodys mechanism

(Thomas, 1996, p 10).

The human body exchanges heat with the surrounding environment in two ways:

heat gain or heat loss.

Fig. 1-1 shows the diagram of the heat exchange between the body and its

surrounding.

Comfort is a subjective issue which varies according to individuals. It is related to

many variables. The main variables related to thermal comfort are illustrated in Fig. 1-2.

These variables can be classified into two categories: environmental variables and

personal variables.

1.3.1. Environmental Variables

1.3.1.1 Air Temperature

Air temperature affects the convective heat exchange between the skin and the

ambient air. The average skin temperature is around 91-93 (33-34 C) in indoor

situations. When the surrounding air temperature is low the body loses heat. However,

with higher temperature the body gains heat by convection. The convective heat

exchange rate depends on the airspeed, approximately in proportion to the square root

of the airspeed. Moreover it depends on the amount of insulation value produced by the

clothing (Givoni, 1998, p 15).

1.3.1.2 Mean Radiant Temperature (MRT)

MRT is the average of the surrounding surface temperatures with which the

human body can exchange heat by radiant transfer (TechAsm).

Fig. 1-1: Heat exchange between man and surrounding (Source: adopted from Olgyay, 1963, p 16).

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Fig. 1-2: The main variables related to thermal comfort. (source: http://www.design.asu.edu/radiant/01_thermalComfort/comfortC_01variables.htm)

1.3.1.3 Airspeed

Airspeed affects the human body in two different ways; firstly it affects the

convective heat exchange between the body and surrounding environment. Secondly it

determines the evaporation capacity of the air, which consequently affects the amount

of cooling from sweating (Givoni, 1976, p 65-66).

The criterion for defining a suitable airspeed may differ from residential to

office buildings. The ASHRAE guide (1985) identifies an upper limit of 160 ft/min (0.8

10

http://www.design.asu.edu/radiant/01_thermalComfort/comfortC_01variables.htm

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m/s) for indoor airspeed. This speed will not cause the flying of papers. However, for

naturally ventilated residential buildings, the airspeed limit can be based on its

contribution to the indoor comfort (Givoni, 1997, p 17).

1.3.1.4 Humidity

The humidity of the air can be expressed in various terms: relative humidity,

absolute humidity, specific humidity, or vapor pressure. The humidity of air does not

directly affect the heat load on the body, but it changes the evaporation capacity of the

air, which affects the cooling effectiveness by sweating (Givoni, 1976, p 63).

The evaporation capacity of air is a function of the air humidity (vapor pressure)

and airspeed. Very low humidity may causes irritation; however higher humidity level

has indirect effects on the human comfort by affecting the evaporation capacity. Higher

humidity decreases the evaporative cooling of the skin, but the body can counter this

decrease by increased sweating (Givoni, 1997, p 15-16).

1.3.2. Personal Variables

1.3.2.1 Clothing Insulation Value (clo value)

Clothing creates a barrier to the convective and heat radiation exchange between

the body and the surrounding environment. It interferes also with the process of sweat

evaporation. In addition it decreases the sensitivity of the body variations according to

the surrounding air temperature and speed. The unit used to refer to the thermal

resistance, insulation, of clothing is called col (Givoni, 1976, p 68).

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1.3.2.2 Metabolism Rate

Metabolism is the process by which food substance combines in the human body

with oxygen to produce the energy required for the activities of various organs in the

body like contracting or stretching the muscles (Givoni, 1976, p 21).

1.4. Comfort Zone

The comfort zone does not have real boundaries. It differs with individuals, type of

clothing, nature of activity, sex, age, and geographical location (Olgyay, 1963, p 18).

In the past there have been several experiments and efforts to define the boundaries

which delimit human thermal comfort.

In 1923-1925 ASHRAE developed the term Effective Temperature Index (ET),

which combines in one number the effect of dry-bulb temperature, humidity, and air

motion on the sensation of thermal comfort (Givoni, 1997, p 23).

Fig. 1-3 shows an example of the relation between effective temperature and

percentage of observations indicating comfort.

Fig. 1-3: Relation between Effective Temperature and percentage observations indicating comfort. (source: Olgyay, 1963, p 18)

1.5. Bioclimatic Charts

Bioclimatic charts help in analyzing the climatic characteristics of a given location

in relation to human thermal comfort. They can also give building design guidelines to

increase the indoor comfort conditions. Such charts are constructed around the comfort

zone (Givoni, 1997, p 23). Examples of bioclimatic charts are Psychrometric and

Olgyay charts.

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1.5.1. Psychrometric Chart

A psychrometric chart is a graph which shows the combination of many physical

properties and thermal prosperities of moist air. It is widely used in air conditioning and

studying the buildings indoor environment in relation to human comfort within a

specific time period.

Moist airs thermodynamic state is uniquely fixed if the barometric pressure and

two independent properties are known. A psychrometric chart can be assembled for a

specific value of barometric pressure. Usually sea-level pressure is used (Threlkeld,

1970, p 179).

The psychrometric chart was developed by Willis Carrier in 1906. Since that

time there have been many improvements and differences. The psychrometric chart can

have various formats and every format shows different properties.

A psychrometric chart may include the following properties of the air: moisture,

dry-bulb and wet-bulb temperatures, relative humidity, humidity ratio, specific volume,

dew point temperature, and enthalpy. In addition it may include the boundaries of the

thermal comfort zones for summer and winter times.

Fig. 1-4: Schematic drawing of the Psychrometric chart (Source: Psychrometric Chart Use, UCONN)

Fig. 1-5: Psychrometric chart with winter and summer comfort zones (source: http://www.coolerado.com/CoolTools/Psychrmtrcs/0000PsychrmtrcLetter.pdf )

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http://www.coolerado.com/CoolTools/Psychrmtrcs/0000PsychrmtrcLetter.pdf

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1.5.2. Olgyay Chart

In 1963, Olgyay developed a bioclimatic chart called by his name; the chart has

the relative humidity as X-axis and temperature as Y-axis. The comfort range is plotted

on the chart. The comfort range is surrounded by a lower limit of fixed temperature (70

F / 21 C) and by an upper limit which is a temperature curve defined by relative

humidity values.

Below the comfort zone (under-heated condition), the chart specifies the

required increase in temperature in order to achieve thermal comfort or the amount of

radiant solar gain required. However, for the upper limit of the comfort zone

(overheated condition) an increase in the airspeed is required to lower the perceived air

temperature by water evaporation.

Fig. 1-6: Olgyay bioclimatic chart, for the U.S moderate zone inhabitation (source: Olgyay, 1963, p 22)

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Fig. 1-7: Olgyay bioclimatic chart, Schematic bioclimatic index (source: Olgyay, 1963, p 23)

1.6. Chapter Summary

This chapter briefly introduces the buildings thermal performance and human

thermal comfort. In addition this chapter presented the terms comfort zone and

bioclimatic charts.

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19

It showed that temperature, relative humidity and airspeed are the main factors that

affect the human thermal comfort. It also showed that buildings design and material

have direct effect on the indoor environment and thermal performance of buildings.

Improving a buildings thermal performance can reduce the amount of energy required

to cool, heat, and ventilate buildings. Many researchers and architects are researching

how to improve this performance. One of the tools used by researchers is called a

datalogger. The following chapter introduces the datalogger technology and examples

of how this technology is used in researching the buildings thermal performance.

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CHAPTER 2: Dataloggers

2.1. Introduction

The intention of this research was to develop a program to visualize the recorded

measurements by dataloggers in relation to a buildings 3D form or layout. The program

is expected to help architects and researchers in studying a buildings thermal

performance over time.

Development in the datalogger industry has made it possible to record temperature,

humidity, voltage, light intensity, and other values in a relatively small device.

Architects and researchers can use dataloggers to study building climatology, thermal

performance, and thermal comfort, recording a buildings indoor environment over

time.

This chapter discusses dataloggers and their applications, use, selection, the

differences between dataloggers and other acquisition systems, and examples of using

dataloggers in building research.

2.2. What is a Datalogger

A datalogger (data logger) refers to an electronic device that records data over time

or in relation to location (Wikipedia, Datalogger). They are used to record

measurements like temperature, relative humidity, light intensity, on/off, open/close,

voltage, pressure, and events .The common datalogger is a small standalone device, is

typically battery powered, has internal memory to store the data, and includes a

microprocessor and sensors (ONSET, What is datalogger). Fig. 2-1 shows an example

of a datalogger and its components.

Fig. 2-1: Front and back view of the ONSETs HOBO datalogger (source: http://www.1800loggers.com/support/manual_pdfs/loggers/2869_J_MAN_HO8_03x_08.pdf)

2.3. Datalogger applications

A datalogger can be used for indoor, outdoor, or underwater applications. Because

of their small size and battery power, they are ideal for applications with field studies,

transportation monitoring, HVAC tests, troubleshooting, quality studies, general

research, and educational science (ONSET, What is datalogger). For example,

dataloggers are widely used to record and monitor temperature and relative humidity

inside buildings.

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http://www.1800loggers.com/support/manual_pdfs/loggers/2869_J_MAN_HO8_03x_08.pdf

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2.4. The Main Selection Criteria for Dataloggers

There are many datalogger types and products on the market. Selecting the

appropriate datalogger depends on the application type, number, type of measurements,

and budget. The following criteria are important when selecting the proper datalogger

for a specific application: type of input signal, number of input channels, sample rate

and memory capacity, and size.

2.4.1. Input Signal

Some dataloggers are designed for a certain input signal type while others can

be programmed for different types of inputs. Examples of input signals are temperature,

relative humidity, dew point, frequency, light on / off and pressure. These are directly

related to the type of sensor the datalogger contains (OMEGA, Introduction to

Dataloggers).

2.4.2. Number of Input Channels

Dataloggers can handle from simple single-channel input to multi-channel

inputs. Some dataloggers can handle hundreds of inputs at one time.

2.4.3. Sample Rate and Memory Capacity

Sample rate defines the number of sample readings per second. Generally,

because dataloggers store the measurements on an internal memory, they have a low

sample rate. The higher the sample rate, the larger memory capacity required or the

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shorter the amount of time can be recorded. The duration between readings and the

recording period affects the size of the internal memory. For example, an application

that requires sample rates of 1 reading per second for one hour period, needs a

datalogger with enough memory capacity to store 3600 samples (1 sample/sec x 1 hour

x 3600 seconds/hour) (OMEGA, Introduction to Dataloggers).

2.4.4. Size

According to the application type, the size of the datalogger becomes important

because of the space limitation and installation location. In public places using smaller

dataloggers makes it easer to hide them. Or the datalogger might have to fit in small

volumes.

2.5. How a Datalogger is used

1. A datalogger is typically connected to a computer via a serial or USB port. A

datalogger proprietary program (see CHAPTER 4: ) is used to launch, initiate,

and setup the datalogger parameters like sample rate interval, start date and time,

etc.

2. The datalogger is disconnected from the computer and deployed to its target

location to collect measurements over a specific time period according to the

application or research needs. During this period the readings are recorded over

time and stored into the datalogger memory to be later downloaded on

computer. Some more sophisticated dataloggers can display a real-time data via

the web or wireless network.

3. The datalogger is connected to a computer. The datalogger proprietary program

is used to transfer the data from the datalogger memory to the computer. The

imported data can be viewed in tabular format or graph view according to the

program capabilities. Generally all datalogger proprietary programs enable the

user to save the data in delimited format to text file; as shown in Fig. 2-2; or

export the data to a spreadsheet or database program.

Fig. 2-2 : Example of datalogger readings in delimited text format

2.6. Datalogger Proprietary Programs

Most datalogger manufacturing companies have their own proprietary programs

(see CHAPTER 4: ) to manage their datalogger devices. The capabilities of the

program differ from one company to another. However, in common most datalogger

proprietary programs handle the following tasks:

1. Launching, initiating and programming the datalogger by setting the sample rate

interval, clearing the internal memory stack, selecting the input channels to

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25

record, setting the start recording date, and resetting the internal clock of the

datalogger.

2. Viewing the datalogger battery status and serial number.

3. Adding description(s) to the datalogger.

4. Importing the recorded data from the datalogger via serial or USB port.

5. Providing a tabular and graph view to display the imported data.

6. Exporting the imported data to spreadsheet or database programs

Examples of datalogger program are presented in section 4.2. of this thesis.

2.7. Datalogger Vs Data Acquisition Systems

Even though the terms data logging and data acquisition are often used

interchangeably, there are some differences between them (wikipedia, Data logger):

1. Dataloggers are standalone instruments and computer independent. They can

be left unattended during the measurement period, and later the recorded

measurements are transferred to a computer, while data acquisition systems

must be connected to a computer or server during the measurement period.

2. Dataloggers typically have a slower sample rate in comparison to a data

acquisition system.

3. Dataloggers are generally considerably cheaper than data acquisition systems.

The unattended nature of dataloggers gives them higher flexibility to be used in

harsh and remote areas. On the other hand, the computer dependent nature of a

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data acquisition system makes it more applicable for applications where it is

possible to install and deploy the infrastructure for this system like building

monitoring or diagnostic system or stationary testing devices.

4. Both data acquisition systems and dataloggers should be robust, but it is critical

that the dataloggers are highly reliable. The absence of human supervision and

the harsh surrounding environment requires that manufacturing companies

ensure that the datalogger is working properly during the measurement period;

the failure to log measurements or any problem in the battery power can affect

the whole purpose of the application or research. In addition, in some

applications the hostile conditions the dataloggers are exposed to may cause

problems when connecting the data logger to a computer, and in some cases it

could be difficult to retrieve the data from the datalogger. However, generally

data acquisition systems are more reliable than dataloggers because of the

presence of human supervision. Moreover data acquisition systems can be used

in controller loop since it can prompt the computer to initiate an action in real

time.

2.8. Using Dataloggers in Building Research. Examples from Previous Published Researches

This section illustrates two examples of research which have used dataloggers as a

part of the research process.

2.8.1. Thermal Comfort in a Sustainable House by Frank Lloyd Wright by Marc Schiler and Sumit Brahmbhatt. (Schiler, Brahmbhatt, 2006 )

This research project studied the aspects of sustainable design and thermal comfort in

the Freeman House designed by Frank Lloyd Wright. Initially there was a data network

called BEEMS monitoring the building. The researchers, Schiler and Brahmbhatt,

decided in addition to use ONSET Hobo, Stowaway, and Maxim iButton dataloggers to

monitor the house indoor environment. The researchers relied on collecting the

measurements from different systems to compare the accuracy and normalization and to

ensure a consistent data measurements for the entire house. The recorded data was

analyzed by using the Olgyay chart.(see Olgyay Chart in section 2-5)

Fig. 2-3: Upper floor plan of the house with indication to the locations of the used dataloggers (source: Schiler, Brahmbhatt, 2006 ) .

27

Fig. 2-4: Lower floor plan of the house with indication to the locations of the used dataloggers (source: Schiler, Brahmbhatt, 2006 ).

Fig. 2-5: the used devices to monitor the house (source: Schiler, Brahmbhatt, 2006 ) .

28

Fig. 2-6: Example of the recorded data presented in time series graph and Olgyay chart (source: Schiler, Brahmbhatt, 2006 ).

As shown, the authors of this research included plans of the researched building

to show the location of the used dataloggers. Even though the graphs in Fig. 2-6 show

the overall performance of the building, its clear from the graphs that some dataloggers

have strange readings. From the graph it is hard to directly find out where these strange

dataloggers are located inside the building and the reader of these graphs should refer

again to the buildings plans back and forth to find a relation between these strange

readings and the their location inside the building. The authors have found that these

29

strange reading were a result of installing the dataloggers in incorrect locations. For

example one datalogger was installed near a lamp. If a 3D presentation of dataloggers

values according to their location distributions inside the building where it was used, it

would be easier to find out and illustrate the reasons behind these anomalous readings.

2.8.2. Energetic analysis of a passive solar design, incorporated in a courtyard after refurbishment, using an innovative cover component based in a sawtooth roof concept by M.R. Herasa, and others. (M.R. Heras and others, 2005)

This paper is about the experimental results and specific thermal and energy

saving analysis from the systematic monitoring carried out to analyze the energetic

performance of a building with an innovative component, which is based on an

optimization of the sawtooth roof concept. (M.R. Heras and others, 2005).

The experiment was held at a building of the University of Almera. Equipment,

thermal and metrological sensors (dataloggers) have been installed. The thermal

performance of the building has been recorded for a year. Analysis has been carried out

by comparing the climatic data and recorded climate measurements.

The sensors were installed in the patio at the medium level of each floor of a

three floor building. All sensors were connected to a data acquisition module. Sensor

numbers and locations are showed in Fig. 2-8 and Fig. 2-9. Moreover, Fig. 2-10 shows

graphs of some of the recorded measurements.

As shown in the illustrated Figures, the authors used plan and section drawings

with symbols to show the locations of sensors (dataloggers). The reader of the graphs

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http://www.sciencedirect.com.libproxy.usc.edu/science?_ob=ArticleURL&_udi=B6V50-4D97FFY-3&_user=1181656&_coverDate=01%2F01%2F2005&_alid=576470978&_rdoc=4&_fmt=full&_orig=search&_cdi=5772&_sort=d&_docanchor=&view=c&_ct=13&_acct=C000051901&_version=1&_urlVersion=0&_userid=1181656&md5=dd46e140b78b9f673112f857e07797eahttp://www.sciencedirect.com.libproxy.usc.edu/science?_ob=ArticleURL&_udi=B6V50-4D97FFY-3&_user=1181656&_coverDate=01%2F01%2F2005&_alid=576470978&_rdoc=4&_fmt=full&_orig=search&_cdi=5772&_sort=d&_docanchor=&view=c&_ct=13&_acct=C000051901&_version=1&_urlVersion=0&_userid=1181656&md5=dd46e140b78b9f673112f857e07797eahttp://www.sciencedirect.com.libproxy.usc.edu/science?_ob=ArticleURL&_udi=B6V50-4D97FFY-3&_user=1181656&_coverDate=01%2F01%2F2005&_alid=576470978&_rdoc=4&_fmt=full&_orig=search&_cdi=5772&_sort=d&_docanchor=&view=c&_ct=13&_acct=C000051901&_version=1&_urlVersion=0&_userid=1181656&md5=dd46e140b78b9f673112f857e07797eahttp://www.sciencedirect.com.libproxy.usc.edu/science?_ob=ArticleURL&_udi=B6V50-4D97FFY-3&_user=1181656&_coverDate=01%2F01%2F2005&_alid=576470978&_rdoc=4&_fmt=full&_orig=search&_cdi=5772&_sort=d&_docanchor=&view=c&_ct=13&_acct=C000051901&_version=1&_urlVersion=0&_userid=1181656&md5=dd46e140b78b9f673112f857e07797ea#aff1#aff1

has to return to the building drawing in order to relate the sensors to their location. It

would be easier to read and compare the measurements if the sensors and their

measurements simultaneously where presented inside the buildings 3D model. The

user could directly relate the changes in measurements of a sensor to its location in the

3D space. Moreover the user could easily compare the readings of different sensors in

the 3D space without going back and fourth between drawings and graphs.

Fig. 2-7: (a) Global view of space conditioned. (b) Global view of building (source: M.R. Heras and others, 2005)

31

Fig. 2-8: Scheme of numbering and location of the vertical lines of measurement into the patio (source: M.R. Heras and others, 2005).

Fig. 2-9: Sensors locations scheme (source: M.R. Heras and others, 2005) .

32

Fig. 2-10: (a) Level 14th February, (b) Level 114th August, and (c) Level 125th June ( source: M.R. Heras and others, 2005)

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2.9. Chapter Summary

This chapter has introduced the datalogger technologies, applications, and the

differences between them and a data acquisition system.

Dataloggers provide a flexible and powerful tool for measuring and recording the

building thermal performance and indoor comfort over time. However, when

downloading these measurements from dataloggers, the measurements have a tabular

data format that makes it difficult to be analyzed. Moreover, the illustrated case studies

imply that incorporating the 3D spatial distribution of the dataloggers inside a building

would be valuable addition when studying and analyzing this tabular data.

In order to study this tabular data, it should be converted into graphical presentation.

The process of transferring data into graphical presentation is called data visualization.

The next chapter discusses the data visualization and the elements of data visualization.

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CHAPTER 3: Information Visualization

3.1. Introduction

The intention of this research is to develop a program to visualize the recorded

measurements via dataloggers in relation to a buildings 3D. The program is expected to

help architects and researchers to study a buildings thermal performance over time.

This chapter will present a brief background about information visualization. The

intention of this chapter is to highlight some aspects of data visualizing which were

helpful to develop the visualization method used inside the developed program through

this thesis research.

3.2. What is Visualization

In the dictionary, visualization means a mental image that is similar to a visual

perception (Dictionary). Visualization communicates with our natural senses and

presents numerous amounts of data in a simple picture that worth a thousand words.

3.3. Why Visualization

Visualization helps our cognitive system to compare, analyze and rapidly interpret

data into meaningful and noticeable patterns that were previously invisible.

A good example to demonstrate the visualization benefits is the 3D presentation

which was made for multiple echo sounder scanning parts of Passomoquoddy Bay (see

Fig. 3-1). The bay is located between Maine in the United States and New Brunswick in

Canada and the tide at this bay is considered the highest in the world. About one million

measurements were made. Usually, this type of data is presented in a nautical chart with

contours and spot sounding. When standard computer graphics where used to visualize

this data, many things became noticeable that were previously invisible on charts. A

pattern of features called pockmarks, which are in a form of lines, easily can be seen.

Moreover, various problems with the data were noticeable such as the linear ripples

which are not aligned with the pockmark; this misalignment implies an error in the data

while taking the measurements (Ware, 2004, p3).

Fig. 3-1: Passamoquoddy Bay visualization. (The Canadian Hydrographic Service) (source: Ware, 2004,p 3)

36

3.4. The Visualization Process

The visualization process includes four stages illustrated in Fig. 3-2. The four stages

are:

Data collection

Data preprocessing and transformation into something understandable.

Graphical image

Human cognitive processing.

Fig. 3-2: A schematic diagram of the visualization process (source: Adopted from Ware (2004, p 4))

37

38

3.5. What Excellent Visualization Should Display

According to Tufte, excellent visualization should display the following:

Show the data Enable the viewer to think about the substance rather than about the

methodology or any thing else. Present many numbers in a small space Make a large data set coherent Encourage the eye to compare different pieces of data Reveal the data at several levels of detail, from a broad overview to the fine

structure Serve a reasonably clear purpose: description, exploration, tabulation, or

decoration. Be closely integrated with the statistical and verbal descriptions of a data set

(Tufte, 1983, p 13).

3.6. Memory, Cognitive System and Visual Perception

Memory has the frame which underlies our active cognition and attention is the

motor (Ware, 2004, p 352). There are three types of memory, iconic, long-term and

working memory. Visual objects of instant attention are held temporarily inside the

visual working memory. The visual working memory is a short term memory and has a

limited capacity which can hold around four objects (Vogel, Woodman, & Luck, 2001).

Our cognitive system starts processing information by extracting the visual scene

features in parallel, later it divides the visual field into regions and simple patterns, and

finally a small number of objects at one time are held by the working visual memory.

These objects are selected based on querying the available patterns, the queries answer

specific questions based on the demand of our attention (Ware, 2004, p 20-22).

39

3.7. Graphical Objects Design (the Glyphs)

This section presents some aspects of designing graphical objects which were

helpful in designing the graphical object used to visualize the temperature and relative

humidity inside the developed program of this thesis.

Graphical object; or what is called glyph; is used to visualize multiple data. For

example, when using a circle to visualize the distribution of a disease on map, the color

of the circle might be used to distinguish between males and females and the circle size

could show the number of patients.

Our eye scans information as a snapshot of each fixation. Objects are scanned in

series one after another. The number of items which can be held in the visual working

memory depends on how the objects are organized and presented inside the visual

scene. For example, Vogel et al. (2001), found that if colors were combined with

concentric squares, then six colors could be stored in the visual working memory,

however if the squares were put side-by-side only three colors could be held.

The shape of the glyph affects the number of objects that can be held in our visual

working memory. For example Fig. 3-3 shows two ways of representing the same data.

The left side of the figure presents integrated glyphs; each glyph is a colored arrow

showing temperature by the arrow color, orientation by the arrow direction, and

pressure by the arrow width. The right side of the figure presents nonintegrated glyphs;

the three quantities are visualized in separated visual objects: orientation by an arrow,

temperature by the color of a circle, and air pressure by the height of the rectangle. The

theory of the visual working memory and the results of Vogel et al. (2001), suggest that

three glyphs (the left side of Fig. 3-3) could be held in the working memory , however

only one glyph (right side of Fig. 3-3) could be held in the visual working

memory(Ware, 2004, p 355-356 ).

Fig. 3-3: when integrating multiple data attributes into a single glyph, more information can be retained in the working memory (source: Ware, 2004, p 356).

The graphical dimensions of the glyph; such as width and color; determine how

this object will be perceived and interpreted by our cognitive system. Graphical

dimensions can be classified into integral and separable dimensions. Integral

dimensions are perceived holistically, for example the hue and brightness of a color are

perceived as one. Graphical dimensions which are not integral are separable such as the

color and height of an object. Separable dimensions is used for analytical tasks were

people have to make a separated judgment about each dimension. Separable and

integral dimensions can be organized as pairs. They can be ranked from most integral to

40

most separable dimensions. Fig. 3-4 and Fig. 3-5 show the ranking of separable and

integral according to dimensions pairs.

Fig. 3-4: the display dimensions pairs ranked in order from highly integral to highly separable (source:Ware, 2004, p 180).

Fig. 3-5: example of glyphs designed according to two display attributes. Top are more integral pairs and bottom are more separable pairs (source: Ware, 2004, p 181).

41

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A glyphs design depends on the data types it presents. Data types can be

classified into categories based on the form of data. There are four main data scales:

nominal, ordinal, interval and ratio. Nominal scale is used to describe data which

has no order such as the type of animals. Ordinal scale describe data organized in

order based on relative relation such as object A is bigger than object B; but it does

not provide information of how much A is bigger than B. Interval scale describes

data based on a continuous and consistent scale with specified units such as meter

and kilogram. Finally, Ratio scale describes measurements based on a reference

value, for example, object A is twice as large as object B. The visualization of a

data should comply with this data type. For example there is no meaning to classify

data with nominal scale by using gradient color.

Based on this design criteria and the purpose of this thesis research, this thesis

suggested that the design of the graphical object which will present the temperature

and relative humidity data could (for more details see CHAPTER 5: ):

- Be simple and clear in order to help the user working memory encodes

more objects.

- Be visualized according to defined query criteria which would help

attracting the user attention. The query criteria could be done by

classifying the data value ranges into categories and assign different

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color for each category. For example values above the temperature

comfort level could have a red color.

- Be presented by using separable dimensions pairs such as height and

color to help the user makes analytical judgments on the data.

- Reflect the data types it presents. Since temperature and relative

humidity have interval scales, the graphical object could reflect the

increase and decrease in values by changing specific graphical

dimension of the graphical object. For example height could reflect the

increase and decrease in values.

3.8. Chapter Summary

This chapter introduced a brief background about the ideal information

visualization. As shown, visualization is important for transforming data into

understandable form. Many researchers have developed guidelines for designing and

producing a good visualization based on understanding the data attributes and the

processes of our cognitive system. Part of these guidelines is the idea of using glyphs to

present multiple data attributes by using visual objects. The simplicity and coherence of

the glyph would help people rapidly perceive and analyze data.

The next chapter will introduce a reviewing of existing programs which visualize

the data recorded via dataloggers.

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CHAPTER 4: Reviewing Existing Programs

4.1. Introduction

The intention of this research is to develop a program to visualize the recorded

measurements via dataloggers in relation to a buildings 3D geometry. The program is

expected to helped architects and researchers to study a buildings thermal performance

over time.

Chapter 1 introduced the concept of thermal performance of buildings, thermal

comfort, bioclimatic charts like Olgyay, and psychometric charts.

Chapter 2 introduced the dataloggers technology, applications, and previous

research that used dataloggers to study building thermal performance.

Chapter 3 Introduced data visualization, the importance of visualizing data, and

components of visualization.

The connection between these chapters presents:

The need for researching the thermal performance of buildings and human

comfort.

The potential of using dataloggers in researching building thermal performance.

How visualization is helpful and important for understanding and analyzing

data.

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This chapter reviews some existing programs that can be used to visualize recorded

measurements from dataloggers. These software programs can be categorized as those

that come with the datalogger and those that do not.

1. Proprietary Programs: Programs associated directly with datalogger hardware

and used to import and visualize data collected from a specific manufactures

datalogger.

2. Other Programs:

a. Programs focusing on interpreting tabular data that can be used to

visualize datalogger measurements.

b. Programs used to visualize a buildings thermal performance.

4.2. Proprietary Programs

4.2.1. General Description

Each manufacturer has its own programs that are associated directly with specific

datalogger hardware. These programs are used to launch and set these dataloggers. The

capabilities of a program differ from one manufacturer to another. However, in general

these programs handle the following tasks:

Launching, initiating, and programming the datalogger by setting the sample

rate interval, clearing the internal memory stack, selecting the input channels to

record, setting the start recording date, and resetting the internal clock of the

datalogger.

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Viewing the datalogger battery status and serial number.

Adding a description to the datalogger.

Importing the recorded data from the datalogger via a serial or USB port.

Providing a tabular or graph view to display the imported data.

Exporting data to spreadsheet or database programs.

The following table shows a comparison between three propriety programs.

Further sections will discuss each program separately.

Table 1: Comparison between the capabilities of three propriety programs

Feature BoxCar TrendReader MicroLabPLUS Launching, initiating and programming dataloggers and viewing the datalogger properties Exporting data to other software like spreadsheet programs Printing and coping graphs and data to other programs Graph views and table views. Wireless communication Communicate remotely with data loggers by using a modem Display real-time values while reading from a datalogger Write mathematical equations to interpret the data Create a graph statistics table, which shows description, maximum, mean, minimum, range, and standard deviation of the data.

Histogram. Overlap loggers icons over a screen shoot of the real working environment. Visual and audio alarms when data exceeds specific thresholds.

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4.2.2. BoxCar version 4.0 by ONSET

This program is shipped with dataloggers manufactured by ONSET. The

program is designed to work with Hobo or Stowaway model dataloggers.

4.2.2.1 General Features

The program can handle the following tasks:

Launching, initiating and programming dataloggers by setting the sample rate

interval, clearing the internal memory stack, selecting the input channels to

record, setting the start recording date and resetting the internal clock of the

datalogger.

Opening multiple data files from different loggers.

Exporting dat