The Pennsylvania State University

The Graduate School

College of Earth and Mineral Sciences

THE GEOGRAPHICAL ANALOG ENGINE: HYBRID NUMERIC AND

SEMANTIC SIMILARITY MEASURES FOR U.S. CITIES

A Thesis in

Geography

by

Tawan Banchuen

© 2008 Tawan Banchuen

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2008

The thesis of Tawan Banchuen was reviewed and approved* by the following:

C. Gregory Knight Professor of Geography Thesis Co-Advisor Co-Chair of Committee

Mark N. Gahegan Professor of Geography Thesis Co-Advisor Co-Chair of Committee

Robert G. Crane Professor of Geography

Dennis W. Thomson Professor of Meteorology

Karl Zimmerer Professor of Geography Head of the Department of Geography

*Signatures are on file in the Graduate School iii ABSTRACT

This dissertation began with the goal to develop a methodology for locating climate change analogs, and quickly turned into a quest for computational means of locating geographical analogs in general. Previous work in geographical analogs either only computed on numeric information, or manually considered qualitative information. Current and emerging technologies, such as electronic document collections, the Internet, and the Semantic Web, make it possible for people and organizations to store millions of books and articles, share them with the world, or even author some themselves. The amount of electronic and online content is expanding at an exponential speed, such that analysts are increasingly overwhelmed by the sheer volumes of accessible information. The dissertation explores techniques from knowledge engineering, artificial intelligence, information sciences, linguistics and cognitive science, and proposes a novel, automatic methodology that computes similarity within online/offline textual information, and graphically and statistically combines the results with those of numeric methods. U.S. cities with populations larger than 25,000 people are selected as a test case. Places are evaluated based on their numeric characteristics in the County and City Data Book and qualitative characteristics from Wikipedia entries. The dissertation recommends a way to convert Wikipedia entries into the Web Ontology Language (OWL) ontologies, which computer algorithms can read, understand and compute. The dissertation initially experiments with Mitra and Wiederhold’s semantic measure to quantify similarity between places in the qualitative space. Many shortfalls are identified, and a series of experimental enhancements are explored. The experiments demonstrate that good semantic measures should employ a comprehensive stop-words list and a complete, but succinct vocabulary. A semantic measure that can recognize synonyms must understand the intended senses of words in a place description. Furthermore, analysts need to be careful with two styles of descriptions: descriptions of places that are (1) created by following a template, or (2) laden with statistical statements can result in falsely high similarity between the places. It is illustrated that scatter plots of numeric similarity scores versus semantic similarity scores can effectively help analysts consider similarity between places in two-space. Analysts can visually observe whether the numeric ranks of places agree with the semantic iv ranks. The dissertation also shows that the Spearman’s rank correlation test and the Kruskal-Wallis test of means can provide statistical confirmation for visual observations. The proposed hybrid methodology enables analysts to automatically discover geographical analogs in ways that strictly numeric methods or manual semantic analysis cannot offer. v TABLE OF CONTENTS

LIST OF FIGURES ...... viii

LIST OF TABLES...... xi

ACKNOWLEDGEMENTS ...... xiv

Chapter 1 Why the Geographical Analog Engine? ...... 1

1.1 Inadequate Numeric Methods and Overwhelming Qualitative Information...... 3 1.2 The Geographical Analog Engine...... 5

Chapter 2 Analogs...... 9

2.1 Definitions...... 9 2.2 Roles of Analogy Making in Human Cognition...... 9 2.2.1 Metaphors...... 10 2.2.2 Classification ...... 11 2.2.3 Abduction...... 12 2.3 Contemporary Usage of Analogs ...... 14 2.4 Conclusions ...... 17

Chapter 3 Similarity: Numeric and Semantic Measures ...... 18

3.1 Ontology ...... 19 3.5.1 Web Ontology Language (OWL)...... 20 3.5.2 Semantic Networks ...... 25 3.2 Similarity Measures...... 29 3.8.1 Semantic Measure...... 30 3.8.1.1 Lexicon Based Measure...... 30 3.8.1.1.1 Mitra and Wiederhold’s Algorithm ...... 31 3.8.1.1.2 Enhanced Version of Mitra and Wiederhold’s Algorithm ... 32 3.8.1.2 Feature-Set Based Measure...... 32 3.8.1.2.1 Basic Synonym Count (BSC) ...... 33 3.8.1.2.2 Synonym Count with a Vocabulary (SCV)...... 34 3.8.1.3 Corpus Based Measure...... 35 3.8.1.3.1 Corpus Based Synonym Count (CBSC) ...... 36 3.8.1.4 Graph Based Measure ...... 36 3.8.1.5 Measure Based on Information Content...... 38 3.8.1.6 Expert Judgment...... 40 3.8.2 Numeric Measure: Euclidean Similarity...... 40 3.8.2.1 Principal Component Analysis...... 41 3.8.2.2 K-Means Cluster Analysis...... 42 3.3 Statistical Tests of Similarity Measures...... 43 3.9.1 Kruskal-Wallis Test...... 44 vi 3.9.2 Spearman’s Rank Correlation Coefficient Test...... 45 3.4 Uncertainty of Similarity Measures ...... 46 3.11 Conclusions ...... 48

Chapter 4 Preliminary Experiment: Six University Cities ...... 49

4.1 Comparison Using Mitra and Wiederhold’s Algorithm...... 54 4.2 Comparison Using Enhanced Mitra and Wiederhold’s Algorithm...... 57 4.3 Conclusions ...... 60

Chapter 5 Numeric Analysis...... 61

5.1 Principal Component Analysis ...... 61 5.2 K-Means Cluster Analysis ...... 66 5.3 Selection of Cities for Subsequent Analyses...... 67 5.3.1 Accounting for the Current State of Wikipedia Entries...... 74 5.4 Conclusions ...... 76

Chapter 6 Semantic Analysis ...... 77

6.1 Synonym Count with a Vocabulary (SCV)...... 78 6.1.1 Results and Discussion...... 79 6.1.2 Discussion of SCV Performance ...... 99 6.2 Corpus-Based Synonym Count (CBSC)...... 99 6.2.1 Results and Discussion...... 100 6.2.2 Discussion of Approaches to Semantic Disambiguation...... 107 6.3 Measure Comparison Using Statistical Tests...... 108 6.3.1 Discussion of Statistical Tests ...... 111 6.4 Conclusions ...... 111

Chapter 7 Summary and Conclusions...... 114

7.1 Revisiting the Research Goal ...... 114 7.2 The Novel Hybrid Methodology...... 115 7.3 Future Research ...... 121

Bibliography...... 125

Appendix A List of Variables in the City Tables and Their Descriptions...... 136

Appendix B Major Variables and Their Loadings for Selected Components...... 149

Appendix C Header Statistics of 490 Wikipedia City Entries ...... 162

Appendix D Snapshots of Wikipedia Entries...... 171

Appendix E Three Wikipedia Sections of Ten Cities...... 194 vii E.1 Ann Arbor...... 195 E.1.1 Economy...... 195 E.1.2 Climate ...... 196 E.1.3 Culture...... 196 E.2 Boston ...... 199 E.2.1 Economy...... 199 E.2.2 Climate ...... 200 E.2.3 Culture...... 201 E.3 Chicago...... 203 E.3.1 Economy...... 203 E.3.2 Climate ...... 204 E.3.3 Culture and contemporary life...... 204 E.4 Houston...... 210 E.4.1 Economy...... 210 E.4.2 Climate ...... 211 E.4.3 Culture...... 212 E.5 Las Vegas ...... 217 E.5.1 Economy...... 217 E.5.2 Climate ...... 218 E.5.3 Culture...... 218 E.6 Los Angeles...... 220 E.6.1 Economy...... 220 E.6.2 Climate ...... 221 E.6.3 Culture...... 222 E.7 Philadelphia...... 228 E.7.1 Economy...... 228 E.7.2 Climate ...... 230 E.7.3 Culture...... 230 E.8 Reno...... 234 E.8.1 Gaming industry ...... 234 E.8.2 Climate ...... 235 E.8.3 Culture...... 235 E.9 San Diego...... 236 E.9.1 Economy...... 236 E.9.2 Climate ...... 237 E.9.3 Culture...... 238 E.10 San Francisco...... 243 E.10.1 Economy ...... 243 E.10.2 Climate ...... 244 E.10.3 Culture and entertainment ...... 245

Appendix F Tables of Intersection Terms...... 251

Appendix G Correlation Coefficient Matrix...... 260

viii LIST OF FIGURES

Figure 1-1: Concept map of a portion of the SWEET physical phenomena ontology..... 6

Figure 2-1: Diagram of the extended example...... 14

Figure 3-1: A semantic network of aircraft types. Redrawn after Giarratano and Riley’s (2005) Figure 2.5...... 27

Figure 3-2: Concept map of a second-grade student. Redrawn after Novak and Musonda’s (1991) Figure 2A...... 28

Figure 3-5: Definitions of the Pacific High and the Azores High from the American Meteorological Society glossary ...... 39

Figure 4-1: Locations of the university cities...... 50

Figure 4-2: Concept map of the Wikipedia entry of State College, PA...... 52

Figure 4-3: Portion of the OWL ontology of the Wikipedia entry of State College, PA...... 53

Figure 4-4: Plots of similarity scores computed with the original algorithm...... 56

Figure 5-1: Improvise visualization of the six clusters: Cluster 1 in yellow, Cluster 2 in blue, Cluster 3 in green, Cluster 4 in purple, Cluster 5 in orange and Cluster 6 in brown. New York City (the only city in Cluster 5) is selected and displayed in red...... 70

Figure 5-2: Scatter plots of the six clusters. The members of Cluster 1 are selected and shown in red...... 72

Figure 5-3: Map of the six clusters. Cluster 1 in yellow, Cluster 2 in blue, Cluster 3 in green, Cluster 4 in purple, Cluster 5 in orange and Cluster 6 in brown. New York City (the only city in Cluster 5) is selected and displayed in red. Note that the map shows an estimated-by-sight location of the Rocky Mountains...... 73

Figure 6-1: Similarity comparison of Los Angeles, CA and other cities based on (a) Principal Component I (PC I) and SCV, and (b) Principal Component II (PC II) and SCV...... 80

Figure 6-2: Similarity comparison of Ann Arbor, MI and other cities based on (a) Principal Component I (PC I) and SCV, and (b) Principal Component II (PC II) and SCV...... 88 ix Figure 6-3: Similarity comparison of Las Vegas, NV and other cities based on (a) Principal Component I (PC I) and SCV, and (b) Principal Component II (PC II) and SCV...... 90

Figure 6-4: Similarity comparison of San Francisco, CA and other cities based on (a) Principal Component I (PC I) and SCV, and (b) Principal Component II (PC II) and SCV...... 92

Figure 6-5: Similarity comparison of Las Vegas, NV and other cities based on SCV and CBSC...... 102

Figure 6-6: Similarity comparison of Las Vegas, NV and other cities based on SCV and CBSC with the modified vocabulary...... 104

Figure 7-1: Similarity comparison of Las Vegas, NV and the ten cities based on Euclidean similarity with respect to PC I and CBSC with the augmented vocabulary...... 119

Figure D-1: Snapshot of Wikipedia entry of State College, PA...... 172

Figure D-2: Snapshot of Wikipedia entry of State College, PA ...... 173

Figure D-3: Snapshot of Wikipedia entry of Ann Arbor, MI...... 174

Figure D-4: Snapshot of Wikipedia entry of Ann Arbor, MI...... 175

Figure D-5: Snapshot of Wikipedia entry of Boston, MA...... 176

Figure D-6: Snapshot of Wikipedia entry of Boston, MA...... 177

Figure D-7: Snapshot of Wikipedia entry of Chicago, IL...... 178

Figure D-8: Snapshot of Wikipedia entry of Chicago, IL...... 179

Figure D-9: Snapshot of the Wikipedia entry Houston, TX...... 180

Figure D-10: Snapshot of Wikipedia entry of Houston, TX...... 181

Figure D-11: Snapshot of Wikipedia entry of Las Vegas, NV...... 182

Figure D-12: Snapshot of Wikipedia entry of Las Vegas, NV...... 183

Figure D-13: Snapshot of Wikipedia entry of Los Angeles, CA...... 184

Figure D-14: Snapshot of Wikipedia entry of Los Angeles, CA...... 185

Figure D-15: Snapshot of Wikipedia entry of Philadelphia, PA...... 186 x Figure D-16: Snapshot of Wikipedia entry of Philadelphia, PA...... 187

Figure D-17: Snapshot of Wikipedia entry of Reno, NV...... 188

Figure D-18: Snapshot of Wikipedia entry of Reno, NV...... 189

Figure D-19: Snapshot of Wikipedia entry of San Diego, CA...... 190

Figure D-20: Snapshot of Wikipedia entry of San Diego, CA...... 191

Figure D-21: Snapshot of Wikipedia entry of San Francisco, CA...... 192

Figure D-22: Snapshot of Wikipedia entry of San Francisco, CA...... 193

xi LIST OF TABLES

Table 4-1: The Six University Cities and Their Universities ...... 50

Table 5-1: Pearson Correlation Coefficient Matrix of Ten Sampled Variables...... 63

Table 5-2: Interpretation of The Selected Components...... 64

Table 5-3: Percents of Explained Variance by Each PC...... 65

Table 5-4: Multi-Way ANOVA of the Clusters...... 67

Table 5-5: Numbers of Cities in Each Cluster...... 69

Table 6-1: Intersection Terms of Los Angeles, CA vs. Boston, MA...... 86

Table 6-2: Intersection Terms of Los Angeles, CA vs. Houston, TX ...... 87

Table 6-3: The Selected Ten Cities in Descending Order of Their Semantic Similarity to the Specified Cities...... 95

Table 6-4: Summary of Correct and Incorrect Synonyms for (a) Top and (b) Bottom Matches...... 96

Table 6-5: Summary of High-IC Terms for (a) Top and (b) Bottom Matches...... 97

Table 6-6: Intersection Terms of Las Vegas, NV vs. Reno, NV ...... 103

Table 6-7: Intersection Terms of Las Vegas, NV vs. Reno, NV ...... 105

Table 6-8: Intersection Terms of Las Vegas, NV vs. San Diego, CA...... 105

Table 6-9: Intersection Terms of Las Vegas, NV vs. Houston, TX...... 106

Table 6-10: Spearman Correlation Coefficients...... 109

Table 6-11: Two-Tailed Significance Values of Kruskal-Wallis Test...... 110

Table A-1: Variables in Table C-1 — Area and Population ...... 126

Table A-2: Variables in Table C-2 — Population by Age, Sex, and Race ...... 129

Table A-3: Variables in Table C-3 — Group Quarters Population and Households...... 133

Table A-4: Variables in Table C-4 — Housing, Crime, and Labor Force...... 136

Table A-5: Variables in Table C-5 — Manufacturing and Wholesale Trade...... 139 xii Table A-6: Variable in Table C-6. — Retail Trade and Accommodation and Foodservices ...... 142

Table A-7: Variables in Table C-7 — Government Finances and Climate...... 145

Table B-1: Principal Component I ...... 150

Table B-2: Principal Component II ...... 155

Table B-3: Principal Component III...... 156

Table B-4: Principal Component IV...... 157

Table B-5: Principal Component V...... 157

Table B-6: Principal Component VI...... 158

Table B-7: Principal Component VII ...... 159

Table B-8: Principal Component VIII...... 159

Table B-9: Principal Component IX...... 160

Table B-10: Principal Component X...... 160

Table B-11: Principal Component XI ...... 160

Table B-12: Principal Component XII ...... 161

Table B-13: Principal Component XIII...... 161

Table B-14: Principal Component XIV...... 161

Table F-1: Intersection Terms of Ann Arbor, MI vs. San Diego, CA...... 252

Table F-2: Intersection Terms of Ann Arbor, MI vs. Reno, NV...... 253

Table F-3: Intersection Terms of Las Vegas, NV vs. Reno, NV...... 253

Table F-4: Intersection Terms of Las Vegas, NV vs. San Diego, CA ...... 254

Table F-5: Intersection Terms of Las Vegas, NV vs. Boston, MA...... 255

Table F-6: Intersection Terms of San Francisco, CA vs. Chicago, IL...... 256

Table F-7: Intersection Terms of San Francisco, CA vs. Philadelphia, PA ...... 257

Table F-8: Intersection Terms of San Francisco, CA vs. Las Vegas, NV ...... 258 xiii Table F-9: Intersection Terms of Las Vegas, NV vs. Houston, TX...... 259

Table G-1: Spearman Correlation Coefficient Matrix...... 261

xiv ACKNOWLEDGEMENTS

Special thanks to the National Science Foundation and the GeoVISTA Center in the Department of Geography at the Pennsylvania State University for providing financial support of this research. The author would also like to thank his co-advisors, Dr. Mark N. Gahegan and Dr. C. Gregory Knight for their valuable advice and mentoring. He appreciates insightful research direction of the other committee members, Dr. Robert G. Crane and Dr. Dennis W. Thomson as well. Dr. Adam Z. Rose, albeit no longer on the committee, well deserves recognition for always encouraging the author to pursue an innovative frontier and for sharing his first-grade economic geography knowledge. Earnest gratitude is due to my GeoVISTA colleagues, especially, Anuj Jaiswal, for their inputs and state-of-the-art computer code. The author would also like to thank his parents and grandparents for their financial support and for keeping on believing, albeit with much doubt, that he could finish this work. His two sisters, Lalita and Pakinee earn similar recognition for not trying to cut off his allowances. Special thanks are due to the Bodiratnangkura family for making his graduate study aboard exciting and fun. The author will not forget many friends at Penn State who would not mind spending time with a blunt, tactless person, and taught him numeral life lessons. Some of these amazing people include Niall Donnelly, Rafael Cancel, Charles Brooks, Jessica Kaplan, members of the Ballroom Dance Club, Master Kaye and his students, and Penn State Geography graduate students. I am forever indebted to you all. Thank you.

Chapter 1

Why the Geographical Analog Engine?

Analogy making plays a central role in human intelligence. French (1995) contended that human recognition of objects or events and construction of knowledge are mostly a process of analogy-making. The following hypothetical, yet realistic, example illustrates how analogy making plays a role in human cognition and knowledge construction. John arrives at London for the first time. At his first glance, he recognized that London is an urbanized, densely populated area, which is very much like New York where John was born and has lived for 25 years. Like New York, John suspects that London has a subway system. So he decides to save some money by taking the subway to his hotel instead of a taxi. As he walks around and looks for an entrance to the subway system, he can feel somebody’s hand in the front pocket of his jeans. But John grabs his pocket and pulls away before the attempt to pull out his wallet is successful. In fact, John has anticipated this from his 25-year experience in New York. He has moved his wallet from the back pocket of his jeans to the front as soon as he arrived and realized how urbanized London is. He suspected that, similar to New York, pick pocketing and robbery are quite common. This example shows that a person can recognize a new thing, in this case London, by making analogy to something that he/she already knows about. Then he/she further constructs knowledge about the new thing by assuming that it possesses other similar characteristics as well. These additional, yet-to-be-verified characteristics can be true or false. If John had gone to Oxford instead, he might have found that his precautionary action was unnecessary. Chorley (1964) noted that analogs have long been used in reasoning and seeking a new perspective on a problem. A good analog is the one that shares some similar features with the study object and is more familiar to the investigator. Chorley suggested three major types of analogs, mathematical symbolization of real world processes, laboratory experiments, and natural historical and/or geographical analogs. In this work, the focus is solely on the last type which refers to places that share a set of similar characteristics and relations today or in the past or future. Chorley stated that no analog is perfect, but none is 2 useless either. Analogs are like torches of different intensities that throw light in many directions. This dissertation develops a novel approach for identification of geographical analogs. Geographical analogs have been widely used in research and applications in geography, on both the physical and the human sides. Swearingen (1987) documented how the French protectorate administration, inspired by the climatic resemblance between Morocco and California, amended their agricultural policy in the 1930’s to adopt and transfer agricultural practices and technologies from California. The amendment necessitated by the failing wheat policy that had been formulated based on the slightly-better-than-hearsay belief that Morocco was the granary of Rome. In fact, the agricultural regions of Morocco receive inadequate rainfall for wheat production. To solve the problem, the protectorate government looked for a more concrete successful example as a model for the new policy and was intrigued by the success of California’s irrigation and its agroclimatic similarity to Morocco. The Moroccan agriculture, as a result of the new policy, transformed from unprofitable wheat production to a profit-making, rapidly growing, and mainly citrus industry. By 1984, Morocco was the world second largest exporter of oranges. Farrigan and Glasmeier (2002) employed one form of geographical analog techniques called a quasi-experimental control method to observed net effects of the U.S. prison development boom of 1980’s and 1990’s. Similar to a true controlled experiment, the method observes changes of a dependent variable from the differences between a control group and a treatment group. Based on some prior-to-1970 statistics of socioeconomic conditions and distance from nearby metro areas, they created a pool of analogous rural U.S. counties. The main premise is that these analogous counties will undergo similar changes under the same socioeconomic influence. They selected from the pool two groups of counties: one with and one without a state-run prison that were constructed between 1985 and 1995. The former served as the treatment group while the latter the control group. The net effects could then be observed as the difference in economic changes of the two groups. Such a method provides a cost-effective alternative to a true social experiment, which is normally expensive and yet yields questionable benefits over its quasi-experimental counterpart since the requirement that that the control group is absolutely unaltered by the treatment is hardly ever achieved. 3 Glantz (1988) pioneered the application of analogs in anthropogenic climate change research. His book, Societal Responses to Regional Climatic Change: Forecasting by Analogy, compiled a myriad of case studies of societal responses to extreme environmental change. These extreme changes were analogs of what could happen as a result of climate change. Each study identified strengths and weaknesses of a society in coping with extreme events, such as sea/lake water-level rise, prolonged drought, and cold spell. Glantz contended that these case studies did not suggest that societies would react the same way should similar extreme events occur in the future. They only inform analysts about how well societies prepare for future extreme climate-related, environmental changes and how societies could improve their preparedness. The lessons learned are not limited to the studied society; they can be applied to other analogous societies as well. Extending Glantz’s idea, this author suggests that analysts can search for contemporary or historical geographical analogs of the future climate and future socioeconomic circumstances of a place under investigation and then use their information to formulate a policy that reduces vulnerability of the place to climate change. Moreover, the author argues that some other societies resembling the case studies, now or later, could experience similar extreme events. Geographical analogs to the case studies can serve as priority targets for policy actions to reinforce their preparedness according to the lessons learned. For example, places vulnerable to hurricanes similar to New Orleans should study the infrastructure failures, communication problems, and lack of aid resources as a result of Hurricane Katrina, evaluate their own vulnerability and make adjustments accordingly.

1.1 Inadequate Numeric Methods and Overwhelming Qualitative Information

At the heart of geographical analog identification is the notion of similarity measurement between places. Existing methods of similarity measurement range from purely quantitative to entirely qualitative. Related to the prison impact example above, one can compute similarity as a multivariate Euclidean distance between a control and a treatment based on various numeric variables, e.g., population growth rate, poverty growth rate and earnings. Such methods are well established throughout the social sciences. 4 However, using numeric distance measures restricts analysts to comparing only those aspects: (i) that can be represented numerically; and (ii) for which actual values are at hand, or can be acquired. Note also that numeric measures of distance, while providing the respectability usually associated with statistical approaches, can be problematic to apply when comparing relationships between places, or accounting for roles that a place plays in a regional economy. Furthermore, in a quasi-experimental control study, the Euclidean measures may suggest a few or many control places as similar to a treatment place. In such a case and where relationships, roles, and other aspects not readily represented numerically are deemed important, an analyst must resort to additional information about those places, such as Web pages, reports, photographs, audio or video interviews, and personal notes. After considering this information, the analyst can then choose the most similar place in his/her opinion as the control location. Each of the aforementioned sources can provide valuable information about a place. To keep the task manageable, this dissertation only tackles textual sources. Objectively evaluating textual information presents a momentous endeavor. Authors can write in one of many styles, using specialized vocabularies in their fields of interest. Readers with various background and professions will interpret such writing differently. Not only can a word have multiple meanings, but many words can also share the same meaning. As a result, determining the sense of a word can be a challenge, let alone determining the intended sense of a sentence or a paragraph. Obviously, analysts need routinely to go beyond numeric methods and analyze qualitative information. Such information has become so abundantly available and easily accessible as a result of the Internet. In most cases, analysts no longer need to make a trip to a library since publications can be accessed via the library’s internet site. An internet search engine, e.g., Google, Yahoo or MSN, will return tens, hundreds and possibly millions of Web pages for a city name. Analysts can no longer manually consider all information they can obtain and hope to analyze. They need a way to automate the analysis and combine the results with those of the numeric methods. 5 1.2 The Geographical Analog Engine

When searching for geographical analogs, past researchers either focus on numerical information or qualitative information of places. This dissertation develops a geographical analog engine that simultaneously uses both kinds of information, providing the users with the best of both worlds. The engine uses ontology, in short a formal representation of semantics (in this case detailing the properties and relationships of places) expressed in a human and machine comprehensible language, to automate acquisition and analysis of qualitative information. The automation allows the users to overcome the innate time and physical limitation of information processing. Figure 1-1 demonstrates an ontology visualized as a concept map. The map displays a portion of the physical phenomena ontology developed by the Semantic Web for Earth and Environmental Terminology (SWEET) project (Raskin and Pan 2005). The ontology includes transient events of Earth system science and related concepts, such as volcanic eruption, hurricane, El Nino, earthquake and terrorist events; and their associated concepts of time, space, earth realm, living elements and non-living elements. A circle in the example concept map represents a concept and each link a relation between concepts. One can represent ontology in multiple ways. Another representation, N-triples, can assist someone in interpreting ontologies if they are not familiar with them. N-triples are similar to an English sentence. A triple has a subject, a predicate, and an object. Links in a concept map are predicates. Child nodes are objects and parent nodes subjects. The subclass relationship is frequently translated to the “is-a” predicate in N- triples. From the physical phenomena ontology, we can say that Lighting is-a AtmosphericElectricalPhenomena or Tornado is-a Atmospheric Circulation. The dissertation uses textual descriptions of places from Wikipedia . The Wikipedia description of a place is not inherently machine comprehensible because it does not explicitly specify meanings of words and relationships between words and sections, for example, what property of a place a word or a paragraph describes; and a section is a subsection of another section or a main section. Hence, a single computer algorithm cannot directly compute similarity of places based on meanings and relationships in text documents. Such an algorithm depends on another algorithm to extract 6 meanings and relationships in a Wikipedia entry, and create the ontology of the entry. For instance, in the same manner as the physical phenomena ontology, the ontology of an entry can specify that a paragraph describes the geography of State College, PA or that Sports is a subsection of Entertainment. Once the ontologies of the entries of interested places have been created, then it is possible for a semantic similarity algorithm to compute similarity between them.

Figure 1-1: Concept map of a portion of the SWEET physical phenomena ontology

The U.S. cities with populations larger than 25,000 people are selected as the test case. With the test case, this dissertation explores various semantic measures, which evaluate similarity of places based on their qualitative information. It begins with a lexical-based measure developed by Mitra and Wiederhold (2002), and gradually improves the measure as its shortcomings are identified. The dissertation later experiments with and enhances a corpus-based measure, also one of Mitra and Wiederhold’s (2002) algorithms in an attempt to determine the intended senses of words. Two final best measures are radically different 7 from Mitra and Wiederhold’s original algorithms. The improvements include removing stop words from descriptions, employing domain vocabularies, decreasing computation time and incorporating a feature-set based model. The geographical analog engine also computes similarity among places in the numeric space, and compares the results with those of qualitative information by means of graphical comparison and statistical tests. The numeric scores are computed as a function of weighted Euclidean distance between places in the statistical attribute space, which is based on the U.S. census statistics in the County and City Data Book (U.S. Census Bureau 2000). Table 1-1 shows the values of a few variables and places from the data book. Note that an endless number of place characteristics exist. Each subset is suitable for a particular purpose. To look for a medium-size, cold-winter town, for instance, one may examine variables such as number of population, average January temperature and heating degree days. The development of a comprehensive set of characteristics for a particular purpose either quantitative or qualitative is beyond the scope of this investigation. The aim here, given a set of place characteristics, is to develop a useful approach to compare them.

Table 1-1: A Few Variables and Places from the Data Book Average # Food Service January Heating Degree City Population Establishments Temperature Days (˚F) Houston, TX 1953631 3902 52.2 1371 Las Vegas, NV 478434 872 45.5 2407 State College, PA 38420 139 24.7 6364

Each set of numeric similarity scores of places are graphically combined with its semantic counterpart by means of scatter plots. The numeric scores are plotted on one axis, and the semantic scores on the other. Scatter plots can demonstrate whether two measures are correlated, and whether the mean of one measure is greater or lower or equal to that of the other. One can also see the ranks of places on a scatter plot. In addition to graphical comparison, statistical tests of correlation and means are performed to confirm visual observations. 8 The outcome of this work provides a novel methodology, accounting for aspects of places that can and cannot be represented numerically, for comparison of places. The methodology enables analysts to discover geographical analogs beyond traditional means of strictly numeric or semantic methodologies. The ability of the methodology to automatically compute similarity speeds up discovery and allows a more extensive search of qualitative textual information. Although, it is engineered and tested for one specific, geographically narrow case, analysts can construct ontologies of places for other cases and domains (either social or physical sciences), and use the same methodology to locate relevant geographical analogs. Chapter 2 will discuss analog theory, explaining the theoretical basis for the application of analogs in this research. Chapter 3 elucidates relevant technologies, similarity measures and statistical tests, and describes the rationale for their selection where appropriate. Chapters 4, 5 and 6 report and discuss the experimental results, focusing on preliminary evaluation of the chosen datasets and similarity measures, systematic analysis of the numeric dataset and thorough evaluation of various similarity measures, respectively. The last chapter revisits the research objectives, demonstrates how well they are met, and recommends future research directions.

Chapter 2

Analogs

Before searching for analogs, it is essential that one understands what they are and why they are useful. This chapter begins by defining the meaning of analogs in the scope of the dissertation, and then explains how analogs play a central role in human cognition. Confusion between analogs and metaphors will be explored. The chapter ends with several examples of how researchers and practitioners widely employ analogs in their work. The relevant technologies and methods for locating analogs will be the topic of the next chapter.

2.1 Definitions

This work uses the meaning of analogs after Chorley (1964) and Hesse (1966). Analogs are “things that possess some common characteristics” (Chorley 1964), which Hesse (1966) referred to as “analogies”. When two things, which can be either physical objects or merely mental concepts, share some characteristics, they are said to be analogous or analogs of one another. The common characteristics can be relations to other things, some specific properties or both. For instance, a dog and a cat, albeit two different species, can be analogous since they have common properties: 4 legs, 2 ears and can both be kept as pets. Bill is a man, and Susie is a woman. They are friends of Steve. Although they have different genders, Bill and Susie are analogous by their friendship with Steve. In other words, they share the same relation ‘is a friend of’ and the same property ‘Steve’. Note that only one analogy is required for two things to be analogous.

2.2 Roles of Analogy Making in Human Cognition

“Human analogy making forms one of the key components of intelligence (French 1995, 1).” 10 Based on the given definition of analogs, this section will illustrate how analogy making plays a vital part in several cognitive processes, that is to say, metaphorical communication, classification and abduction. A full review of mental reasoning is beyond the scope of this dissertation; nevertheless the reader will, hopefully, appreciate the pervasiveness of analogy making in human cognition and reasoning. They will understand that metaphors are more than just analogs, and how classification can be stated as a process of grouping together analogs. It will be shown how analogs are the sample objects from which one can induce the properties of a population, and how one can generate hypotheses via analogy making.

2.2.1 Metaphors

The terms ‘analog’ and ‘metaphor’ tend to be used interchangeably in everyday language. The author proposes that the distinction should be made clear by following Black (1962) in Models and Metaphors. According to Black, metaphors are analogs with additional characteristics. Similar to analogs as defined herein, Black stated that a metaphor always involves two systems with some similarities. The first is labeled primary and is described purely by observational statements. The other is secondary and is described by observational statements as well as by familiar theories. One additional characteristic is that all concepts used in the descriptions must be understood by all parties involved, or else the audience will not understand the intended meaning of a metaphor, or worse they will not understand it at all. Black (1962) then explained that humans interpret metaphors by transferring culturally accepted theories of the secondary system to the primary system. For example, the metaphorical expression that “this issue is not written in stone” is interpreted by transferring the well-known properties of stone, namely, (1) solid and (2) resistant to change, to ‘this issue’. Another additional characteristic is that a metaphor should sound ridiculous when taken at its face value. Considering the same example, nobody would actually write an issue at hand on a stone tablet. Doing so would not miraculously make an issue more certain either. Therefore, the example expression is quite absurd when taken literally.

11 Black (1962) also distinguished two types of metaphors: substitution and interaction metaphor. A substitution metaphor can be replaced by an explicit description of the analogous properties. Arguably, the given example is of the substitution type. We can rewrite the statement as “this issue is not solid and resistant to change.” In an interaction metaphor, the extent of analogy is not known. The analogy itself evolves and expands as one contemplates the primary and the secondary systems. Examples of this type are any interesting metaphors used in science of which their fruitfulness is to be determined by additional research, such as life evolution for landform development, a jigsaw puzzle for plate tectonics, and the gravity model for urbanization. Davis (1899) conceptualized a land area as a living thing that grows and is altered by outside factors. The evidence is in how Davis called areas that were transformed little by various processes ‘young’, and those that had greatly transformed ‘mature’. It is possible that the metaphor inspired him to further search for the genetics of landform. In fact, he used the term ‘genetics’ for three quantities, namely, structure, process and time that determine the form of a land area.

2.2.2 Classification

In Posterior Analytics, Aristotle indicated how classification is based on analogy (Barnes 1994, 98a). Aristotle stated that the names of properties that place things in the same classification were coined by analogy and handed down to us. Hesse (1966) further explained Aristotle’s point, suggesting that Aristotle had created the term ‘osseous nature’ to fill the gap in the Greek language, which did not have the word ‘vertebrate’. Aristotle placed the fish’s spine, squid’s pounce and animal’s bone in the same classification because they share this osseous nature. The author is not asserting that the names of all properties were coined by analogy; he merely attempts to reveal how classification is deeply supported by analogy and how long ago this was recognized. Modern classification systems, such as the Köppen-Geiger climate classification, also involve analogy making. Köppen-Geiger demarcates climate zones by drawing on the world map boundaries of areas with common ranges of property values (Christopherson 2003). The system takes into account average monthly temperatures, average monthly precipitation,

12 and total annual precipitation. Translated by the given definition of analogs, each climate zone encompasses area analogs with property values falling in the same ranges, with the boundaries selected to delineate major biome regions. For instance, the coolest months of two tropical climate analogs must be warmer than 18 degrees Celsius. Similarly, we can also view computational classification methods as effectively identifying and grouping analogs. Gerstengarbe et al. (1999) applied a non-hierarchical, minimum-distance clustering method to classify the climate of Europe by monthly and annual means of precipitation, and surface air temperature. They obtained the data from 228 meteorological stations over the time period between 1979 and 1992, and determined by experimentation what they believed was the optimum number of climate regions, 11. The method depended on a distance matrix, from which the closest stations were selected to form climate regions. The distance was computed as the Euclidean distance in the chosen- variables space between individual stations and each centroid of the climate regions. The author argues that the distance measure essentially evaluates the similarity between stations based on their properties — the chosen variables. As a result, the stations in a resulting climate region share analogous properties, and therefore are analogs of each other by the given definition.

2.2.3 Abduction

Abduction is a mode of human reasoning first described in semiotics and logic by Charles Sanders Peirce (1955). Since then a number of scientists have been advocating abduction as one means for hypothesis generation (Overholt and Stallings 1976, Kuipers 1999, Sowa 2004). Sowa (2004) posited that abduction generates a new hypothesis to explain some observation(s) by searching existing knowledge for similar facts that can help explain the new observation(s). The following hypothetical example demonstrates how abduction works.

13

Surprising Bladder cancer rates are high in X Fact geology, Y water service, and Z nearby industries Background Arsenic exposure is high in X Knowledge geology, Y water service, and Z nearby industries Hypothesis Arsenic exposure causes bladder cancer

First, the surprising fact about the bladder cancer rates is noticed — the high rates tend to occur in X geology, Y water service and Z nearby industries. Note that X, Y and Z denote types (e.g., karst geology, groundwater service, textile industry). Searching background knowledge, it is found that the arsenic exposure is also high in similar regions. Then by abduction, the analyst hypothesizes that arsenic exposure causes bladder cancer. Sowa (2004) stated that locating relevant background knowledge is a process of analogy making. In this case, the analyst searched for another phenomenon that shares the surprising characteristics of bladder cancer. Two features of analogy proposed by Hesse (1966) can further illuminate the understanding of abduction. Hesse proposed that two types of relations exist among analogs. One is composed of the similarity/difference relations associating properties of one thing to properties of another thing, namely, the analogies this review has been discussing about. The other type is composed of the causal relations among the internal properties of an object. The following example, which extends the above example, will illustrate how the latter type of relations can also constitute the analogies between objects and how an analyst generates hypotheses from them. Figure 2-1 shows the extended example. The similarity/difference and causal relations are part of the background knowledge. Possible examples of causal relations in the arsenic exposure case are “the Y water service withdraws raw water stored in the X geology” and “the Z nearby industries discharge arsenic- containing waste water into the raw water deposit.”

14

Arsenic Exposure Bladder Cancer Similarity • X geology • X geology Relations • Y water service • Y water service • Z nearby industries • Z nearby industries Causal Relatios Difference Causal Relatios • can cause a disease Relations • is a disease

Figure 2-1: Diagram of the extended example.

Two related hypotheses can be formulated from the background knowledge and tested: (1) the suggested causal relations are valid for the bladder cancer case as well, and therefore the raw water in the bladder cancer case is also contaminated with arsenic; and (2) arsenic exposure is the cause of bladder cancer. The first hypothesis is generated by suspecting that the bladder cancer case may also have the causal relations of the arsenic exposure case. If we think of causal relations as properties of a case, the analyst inferred unknown properties of the bladder cancer case from the known properties of the arsenic exposure case. How the analyst arrived at the second hypothesis seems more complex. It involves more than just transferring a missing property, and likely a matter of synthesizing all available information. It is worth noting though that two properties with a difference relation are the most useful in this particular case. The analyst produced the second hypothesis by combining and reinterpreting two dissimilar properties: “can cause a disease” and “is a disease”.

2.3 Contemporary Usage of Analogs

Several scientists, such as Chorley (1964), Hesse (1966) and Oreskes (2003), have endeavored to raise awareness among the scientific community to the fact that scientific models are analogs of the study subjects. Chorley (1964) classified analogs or models into three categories: mathematical, experimental, and natural models. Mathematical models are systems of abstract mathematical symbols that represent simplified, conceptual models of

15 pieces of the real world. Experimental models are tangible structures of simplified conceptual models. Lastly, natural models are real-world systems that resemble the systems of interest and are more familiar to scientists. Note that Chorley’s model building requires scientists to first develop conceptual models by imagining in their mind how natural systems work, and then to translate their imagination into these three types of models. Any of these models are suitable for the systems they represent because they are analogous to the systems in some ways, such as behavior, geometry and composition. Therefore, they are the analogs of the systems. Oreskes (2003) recently contended that the word ‘models’ increasingly refers to solely computer models of natural systems, e.g., groundwater, weather, and urban sprawl. In the field of climate change research, Meyer et al. (1998) suggested the urban heat island as a contemporary analog to climate change. Similar to human induced climate change, the weather around a metropolitan area warms up a few degrees Celsius. Urban settings also affect precipitation and extreme weather events. Changnon (1992) posited that (1) we can learn to adjust to human induced climate change by studying how several metropolitan areas are coping with urban heating; (2) urban heat islands emphasize the significance of developing scientifically credible methods to identify changes within the noise of climate variability; and (3) the problem shows the lack of appropriate data and impact methodology, as well as the necessity for multidisciplinary study to understand impacts of climate change on hydrology, regional economy, and human activities. Comparative case studies exemplify another usage of analogs. Knight (2001) stated that comparative case studies identify or evaluate universality over many cases and employ analogs as one of their organizing strategies. When using the analog strategy, an analyst selects cases that are similar in some way and investigates further commonality. Knight noted that cases do not need to overlap in time or space. They can merely share a conceptual linkage, such as various societies’ responses to acid rain, climate change or drought. Each response can serve as a case sharing with other cases its associated phenomenon, namely, acid rain, flood or drought. For example, Knox (1995) studied a group of world cities, which were identified to be cores of economic and cultural globalization. He further found that all such cities are sites of leading global markets of commodities, bond, and equities; are locations of international high-order business service centers; and are headquarters of non-governmental organizations and inter-governmental organizations.

16 Diamond (2005) chose a few prehistoric and modern societies that had and had not collapsed from places he had read about or visited. He explored the chosen places to answer such questions as: “how could a society fail to have seen the dangers that seem so clear to us in retrospect?”; “can we say that their end was the inhabitants’ own fault, or that they were instead tragic victims of insoluble problems?” and “how much past environmental damage was unintentional and imperceptible, and how much was perversely wrought by people acting in full awareness of the consequences?” Analogs can also be employed to conduct a controlled experiment. Conducting a true controlled experiment in social studies, even if possible, is often prohibitively expensive (Cook and Campbell 1979). Alternatively, social scientists often employ quasi-experimental control methods. Isserman and Rephann (1995) evaluated the effectiveness of the Appalachian Regional Commission (ARC) on promoting the Appalachian Region economy. In order to measure the effectiveness, they needed to know how the region would have developed without ARC and compared how the region had changed because of ARC. Certainly, development without the ARC cannot be studied within the region itself. Instead, Isserman and Rephann looked at counties analogous to Appalachian counties, but at least 60 miles away from any of them. They considered similarity in terms of aspects that led to the establishment of ARC such as spatial isolation, economic structure, poverty, and stagnation. Farrigan and Glasmeier (2002) utilized the same method to study the economic impacts of the prison development boom on persistently poor rural places. The commonalties of analogs are not the only valuable aspect in science; the differences are as well. Hesse (1966) termed common properties positive analogy, the different properties negative analogy, and those that are neither as neutral analogy. One comparative study on serologic profiles of virus hepatitis B (HB) clearly illustrates the value of neutral and negative analogies. Fang et al. (2001) compared HB cases in China and Korea. They found that the HB positive rate was highest among the Korean-Chinese (12%), followed by the Han-Chinese (7.2%) and the Korean (4.1%), respectively. However, the HB infection rate, which included people who had recovered from the disease, was highest among the Korean (78.6%), followed by the Korean-Chinese (77%) and the Han-Chinese (60.7%), respectively. They attributed the difference to the Chinese and Korean vaccination policies — a negative analogy. The Korean government mandated all Koreans to receive vaccination, while the

17 Chinese only mandated newborns. That explains why Korea at the time of the study had fewer cases of HB positive than China even though it used to be a big problem in Korea. Fang et al. remarked that lifestyle and diet can also be major contributing factors, but the necessary data were not collected in the study. They were not certain whether there were any differences in lifestyle and diet, and therefore these two parameters are neutral analogies. Hesse (1966) posited that it is through demystifying neutral analogies that scientific innovations emerge.

2.4 Conclusions

The literature review above defines the meaning and usage of analogs in this work. It denotes the possibly confusing usage of the word “analogy”, which means a common characteristic of analogs in this work, and is not synonymous with the word “metaphor”. The review describes many useful roles of analogy making in human cognition, and provides examples of contemporary application of analogs in geography, economics, epidemiology and climate change. Chapter 3 will explain two types of knowledge representation that can encode qualitative place characteristics in formats that machine can understand, and then describe various semantic similarity measures that can compute on them.

Chapter 3

Similarity: Numeric and Semantic Measures

This chapter discusses and, where appropriate, describes technologies and methods relevant to analog searching. It begins with the topic of knowledge engineering, followed by a section on various similarity measures, a section on details of the statistical methods employed in the dissertation and a section on uncertainty of similarity measures. The next section, Section 3.1 will introduce ontology, the underlying concept that is utilized here to encode place descriptions in a human- and machine-comprehensible way. Multiple languages exist for writing ontology. Section 3.5.1 describes one ontology representation language, the Web Ontology Language (OWL). It explains how the majority of today’s Web pages are not machine comprehensible, and hence are not amendable to various automatic place comparison methods explored by this dissertation. Section 3.5.2 depicts another ontology representation language, Semantic Networks. Semantic Networks, albeit machine comprehensible, were designed with better human comprehension in mind. Therefore, they are suitable for a place analog application, which requires human interpretation of place ontologies. Section 3.1 covers necessary concepts and technologies in knowledge engineering that will be employed in Chapter 4 to translate Wikipedia pages into a machine-computable format. The author assumes that the reader has some basic understanding of HyperText Markup Language (HTML), Extensible Markup Language (XML) and logics. By the end of the section, the reader should be adequately equipped to comprehend the suggested translation process. Section 3.2 then describes various semantic and numeric similarity measures that work with ontologies. Section 3.3 explains statistical tests of means and correlations, which will be employed in Chapter 6 to compare similarity measures. Although, this dissertation does not quantify uncertainty in similarity measures, Section 3.4 depicts possible errors in the chosen datasets and similarity measures, and suggests a few methods to quantify them. 19 3.1 Ontology

In philosophy, ontology is the science of the nature of being, or types and structures of things in reality (Smith 2003). The early work can be traced back to Aristotle and Socrates (Lewis 1994). Computer scientists have adopted the name and the principles, and defined ontology according to their own applications (Guarino and Giaretta 1995). Guarino (1997) posited that the most cited definition is “an ontology is an explicit specification of a conceptualization” (Gruber 1995). Conceptualization refers to human abstraction of physical things, abstract concepts, entities and relationships among them that exist in a domain of knowledge and can be represented by a declarative formalism. Ehrig (2007) observed that Gruber’s definition was frequently extended as “an ontology is an explicit, formal specification of a shared conceptualization.” In contrast to Gruber (1995), Ehrig (2007) identified the conceptualization as identification of concepts relevant to a phenomenon and construction of an abstract model with them. He also specified the terms ‘explicit’, ‘formal’ and ‘shared’: explicit denotes that an ontology defines types and usage restrictions of concepts; formal suggests that an ontology is machine comprehensible; and shared implies that an ontology contains consensual knowledge of a group of people. As contended in Section 3.2.7.6, analog searching must involve humans in the foreseeable future. Hence, it is vital for ontologies in this work to be readable by humans as well. Therefore, this dissertation refers to ontology as:

an explicit, human and machine-readable specification of shared conceptualization.

The following two subsections will describe two ontology representation languages that are employed by the dissertation. One of them is textual, and the other is visual. It will be shown that the textual representation gears towards machine readability, while the visual better supports human readability.

20 3.5.1 Web Ontology Language (OWL)

The World Wide Web Consortium (W3C) has endorsed OWL as the ontology representation language for the Semantic Web (McGuinness et al. 2002). As a result, OWL becomes the natural choice for this dissertation, which targets the Web and the upcoming Semantic Web as the main potential source of qualitative information. Understanding OWL requires familiarity with the Resource Description Framework (RDF), XML, frames-based knowledge representation and Description Logics (DL). Each of these technologies requires an in-depth explanation, for which the readers are referred to the given sources. This review will only demonstrate how they work together in OWL. An OWL ontology can be written as a set of RDF triples (Bechhofer et al. 2004). An RDF triple is composed of three elements and typically written in the following manner (Bechhofer et al. 2004):

Subject Predicate Object

Subject is either a resource or a blank node, and object can be a resource, a blank node or a literal. Predicate is a relation between the subject and the object. Resources and literals both refer to identifiable abstract or physical things, but only resources can be identified by Uniform Resource Identifiers (URI). A relation is also a resource, and therefore must be identifiable by a URI. A URI comprises of components, such as a protocol, a server, a path, a file name and a fragment (Berners-Lee et al. 1998). Together they make up a unique identification of a resource. Below is the URI of a resource in the wine ontology . The URI points to a type of wine called Chenin Blanc.

http://www.w3.org/TR/2003/CR-owl-guide-20030818/wine#CheninBlanc

protocol server path filename fragment

21 Instead of pointing to things somewhere else, literals are strings that directly provide descriptions of things. The following snippet of the wine ontology shows a triple with a literal object:

base:WineDescriptor rdfs:comment “Made WineDescriptor unionType of taste and color”

literal object Prefixes base: — http://www.w3.org/TR/2003/CR-owl-guide-20030818/wine# rdfs: — http://www.w3.org/2000/01/rdf-schema#

The triple uses the prefixes “base:” and “rdfs:” to abbreviate the URIs of the subject and the predicate. The predicate indicates that the literal string is a comment of the subject. This triple can be translated into natural language as “WineDescriptor has a comment that it was made as a union type of taste and color”. Lastly, blank nodes are neither resources nor literals. Blank-node subjects or objects are given unique identification strings, which are generated by a unique string generation algorithm and are usually meaningless (e.g., $$uirk278 or nk87jk2) to avoid duplication with those of other blank nodes within the same ontology or other ontololgies. RDF is merely an abstract data model; it needs another specification for serialization — representation of an abstract model on electronic media, e.g., computer memory and hard disks (McBride 2001). The W3C recommended XML for RDF serialization (Klyne and Carroll 2004). XML is today’s standards for marking up Web documents in a machine readable way, which is its main advantage over HTML (Hunter et al. 2007). HTML was designed only to instruct a web browser how to display a document. It does not have a mechanism for encoding what a string on a Web page refers to. For instance, a simple HTML page that displays a name “John Doe”: