In Search of Fear

DEPARTMENT OF ECONOMICS Uppsala University Bachelor Thesis Author: Karl Lindberg Supervisor: Mohammad Sepahvand Spring Term 2020

In search of fear

A study examining the potential of using Google Trends data to estimate the fear of crime in Sweden during 2011-2019

Abstract Fear of crime is an important topic in research as well as in public opinion. However, data on fear of crime is limited and difficult to collect, being heavily reliant on surveys with different methods of operationalization yielding different results. This paper aims to investigate if an alternative method can be used to estimate fear of crime. Using a large representative unique data on fear of crime from Google Trends, I analyze if fear of crime can be estimated in Sweden during years 2011-2019, using The Swedish Crime Survey as benchmark. The results show that the method is accurate for country-level and the most populated regions of Sweden, but less so for the lesser populated regions. This method can be used to estimate fear of crime in a time- and money-efficient way, producing daily estimates at little to no cost.

Keywords: fear of crime, Google Trends, metadata

Acknowledgement: I would like to express my utmost appreciation of my supervisor Mohammad Sepahvand, for his inspiration and guidance.

Abbreviations

Brå – Brottsförebyggande Rådet [Swedish National Council for Crime Prevention] NTU – Nationella Trygghetsundersökningen [The Swedish Crime Survey]

Table of contents

1. Introduction ...... 4 2. Previous studies ...... 6 2.1 Previous studies using Google Trends data ...... 6 2.2 Previous studies on fear of crime ...... 7 3. Theoretical framework and concepts ...... 9 3.1 Wilson’s model of information behavior ...... 9 3.2 Consequences of fear of crime ...... 9 3.3 Change in information searching behavior as a consequence of fear of crime ...... 10 4. Method ...... 11 4.1 Choice of method ...... 11 4.2 Description of method ...... 11 4.2.1 Description of NTU index...... 12 4.2.2 Description of Google Trends index ...... 12 4.2.3 Advantages and disadvantages with the chosen method ...... 14 5. Data ...... 15 5.1 Creation of the NTU index ...... 15 5.2 Creation of the Google Trends index ...... 15 5.3 Data characteristics ...... 19 6. Empirical strategy and results ...... 22 6.1 Fit of Google Trends index ...... 22 6.2 Information searching behavior during an exogenous shock ...... 23 6.3 Potential of Google Trends data in estimating fear of crime ...... 24 7. Discussion and concluding remarks ...... 27

References ...... 28 Appendix A ...... 30 Appendix B ...... 32 Appendix C ...... 36 Appendix D ...... 44

1. Introduction The share of the world’s population that use the internet has been steadily increasing during the 2000’s. The International Telecommunication Union (ITU), a United Nations agency specialized in information and communication technologies, estimates that the share of the world’s population that use the internet is 53.6 % in 2019, meaning it has more than doubled in the last 10 years. For developed1 and developing countries, the share in 2019 is estimated to be 86.6 % and 47 % respectively (UN 2019).

Given the large increase in people that use the internet, the traffic going through search engines such as Google has also increased dramatically during recent years. Google’s share of the online search engine market equals around 90 % in 2019, according to both Statista (2019) and Statcounter (2019). In Sweden, 61 % of the population use Google Search daily, while 85 % use it at least once a week (Internetstiftelsen 2019). This makes them the most dominant actor for web searches by far, and also the most potent supplier of web search metadata, i.e. data on how these searches are conducted, due to the large amount of traffic handled by this agent. In 2006 Google launched Google Trends, a service which provides the user with a tool to determine relative popularity of a specific search query within a certain area of observation, during a specified time period.

Research on fear of crime in Sweden and elsewhere has predominantly been conducted through surveys (see for example, Collins, 2016; Torstensson Levander, 2007). Collins mentions that the methodological approach of the surveys differs greatly in terms of how fear of crime is operationalized which also affect the results obtained, thus this is often acknowledged as a problem within the field of study. The author also states that the complexity of the subject heavily stems from the evidence that a majority of people’s fears have very little to do with the risk of being victimized, but rather it is the perceptions that drive a person’s fear of crime (2016).

This paper aims to evaluate if Google Trends data can be used to estimate the fear of crime on national or regional level in Sweden during the years 2011-2019. Because of differences in how Google Trends handled geographical linking of searched made in 2011 it was not possible to use prior years for this study. To evaluate the research question an index based on the Google

1 Using the UN M49 definition of ‘developed’ and ‘developing’ 4

Trends data is created. The estimates’ correlation with the index based on results from the Swedish Crime Survey [Nationella Trygghetsundersökningen, hereinafter referred to as NTU] is used to evaluate the performance of the Google Trends index in this context. Furthermore, this paper examines the potential of using Google Trends data over survey data to evaluate fear of crime. Also, as Google Trends provides more data points than NTU, an attempt to determine the seasonal variation in fear of crime will be made.

The results show that Google Trends can indeed be a powerful tool in estimating the fear of crime in Sweden, but that it is dependent on large amounts of data. On country level the results from the Google Trends index are in line with the NTU index, producing a statistically significant correlation of 0,83. On regional level the largest county, Stockholm county, produce a statistically significant correlation of 0,88 while the smallest counties tend to produce negative correlations and/or less statistically significant results. Whether this discrepancy in results for larger and smaller areas depends on the smaller Google search volumes or how NTU results are weighted differently for smaller areas is something that this paper, while also not being in its scope, has not been able to determine.

This paper strives to contribute to the field of study by broadening the methods used to measure fear of crime. To the best of my knowledge fear of crime has never been measured using Google Trends data prior to this paper. In this context the contents of this paper adds further insight into the research on fear of crime. Using Google Trends data provides a time-efficient method of estimating fear of crime, both over longer periods of time as well as based on current events, whereas survey-based estimations require more time and work to complete.

The remainder of the paper is structured as follows: In section 2 previous studies using Google Trends data as well as studies on fear of crime will be presented. Section 3 covers theoretical frameworks and concepts. This is followed by section 4 that examines the method, and section 5 that specifies the data as well as the construction of the Google Trends and NTU indexes. In section 6 the empirical strategy and results will be presented. Finally, section 7 is dedicated to a discussion of the results, as well as concluding remarks.

2. Previous studies As the method used in this study, i.e. using Google Trends data to forecast perceptions or other relevant indicators, is of great importance to how this study is conducted, it will be equally emphasized as the actual topic studied; fear of crime. Thus, this section is divided into two parts. First, previous studies using Google Trends will be presented. Second, previous studies on the topic of fear of crime will be presented. No previous studies using Google Trends data to estimate fear of crime have been identified by the author.

2.1 Previous studies using Google Trends data Due to Google’s dominance as a search engine, the metadata from searches conducted using their services is a useful tool for researchers to use. In late 2009 a paper about how influenza epidemics could be detected using search query data from Google was published in Nature (Ginsberg et al. 2009). This is regarded as one of the most influential papers which uses Google Trends data, while also being one of the first to do so. The authors concluded that the metadata could be used to survey influenza activity, with estimates being up to date every day. This was compared to the other systems in place at the time. Previously they had relied on manual reports from hospitals about increases in incoming patients with symptoms and which required 1-2 weeks to provide an estimate. This demonstrated the potential of using search engine metadata for research which previously had used more conventional approaches such as self-reported survey data, and paved the way for further research using Google Trends data.

In 2012, in a paper by Choi et al. Google Trends is used to forecast economic indicators. The authors attempted to create models that would forecast the monthly sales of motor vehicles and parts for years 2004-2011. Adding Google Trends data to the already existing models they were able to forecast the sales with a general improved accuracy of around 10 %. During the period of the Great Recession the model including Google Trends data provided forecasts with an improved accuracy of 21 %. Similarly, the authors found that including Google Trends data in models forecasting unemployment benefits in USA improved the models ability to predict turning points in the trends. Further, also Choi et al. (2012) attempted to model the amount of tourists visiting certain countries based on Google Trends. Using data from official tourism boards to evaluate their model they were able to achieve an R-squared of 73,3 for eight out of nine of the countries included in the predictions.

Preis et al. (2013) is another study that attempted to explain large-scale trading behavior in financial markets using data from Google Trends. Search queries are ranked in terms of financial relevance based on how frequently they appear in the online edition of the Financial Times. They find that using a strategy based on Google Trends analysis yields a higher return compared to using random strategies or the buy-and-hold-strategy (Preis et al. 2013).

As demonstrated by the studies mentioned Google Trends can be used to estimate behavior that has big implications on everyday life. These studies demonstrate that using Google Trends data outperforms survey and other forms of self-reported data. Being able to predict the amount of people seeking medical care for influenza symptoms with virtually no time-lag at all could potentially be a very potent tool for hospitals, as well as predicting other variables that occur in today’s society.

2.2 Previous studies on fear of crime In the meta-analysis Trygghet, säkerhet, oro eller risk? [Safety, security, worry or risk?] (Torstensson Levander 2007) the author Marie Torstensson Levander operationalizes the concept of fear of crime, and examines how the research on the topic had been conducted thus far, both in Sweden and in the UK. Levander concludes that the purpose of this field of study has been to capture how values and everyday life are affected by the occurrence of crime, whether the respondents themselves are exposed or just witnesses to the vulnerability of others (2007).

Nicole Rader writes in her paper Fear of Crime (Rader 2017) about the paradox of fear of crime. This paradox is the fact that fear of crime does not always match a person’s risk of being the victim of a crime. She mentions how research, as a consequence of the paradox, have begun to examine the predictors of fear of crime. This involves predictors on the individual level (gender, age, etc) as well as contextual predictors (neighborhood disorder, social cohesion, etc). While there is still an ongoing debate on how fear of crime should be measured (i.e. should survey questions mention specific crimes or should it be generalized), Rader argues that the best measurement of fear of crime; should be focusing on the concept of worry, should be crime specific, should be location specific, and should measure intensity of the fear (2017). She concludes that while early research on the topic focused on issues regarding conceptualization

7 and operationalization, more recent work has been targeting predictors and consequences of fear of crime.

A gendered perspective on fear of crime is presented by May et al. (2010), where they state that out of the most common predictors used in fear of crime research (i.e. age, race), gender has proven to be the most stable predictor. While men are more often victims of crime, women self- report higher levels of fear of crime than men. In research this is refered to as the “gender-fear- paradox” (a more specific version of the paradox mentioned in the Rader (2017) article). There has been several attempts to explain this, with the most recent being the so-called “shadow hypothesis” theory, according to May et al. (2010) originally presented by Mark Warr in 1984. The theory suggests that this disproportionate fear of crime stems from the fear of being a victim of sexual assault, which consequently spills over to a fear of all crime. May et al. (2010) states that much of the literature has focused on why women have this perceived unreasonable fear of crime, rather than why men have lower levels of fear of crime than expected, suggesting that more research on this is needed.

In Sweden, the Swedish National Council for Crime Prevention (Brottsförebyggande rådet, Brå) conducts a yearly survey of attitudes towards different aspects of crime externalities including 200,000 people (Molin et al. 2019a). This survey, named “The Swedish Crime Survey” [Nationella trygghetsundersökningen, NTU], had an approximate of 73,500 participants in the 2019 edition. The questions asked in the survey aim to, among other things, provide key indicators on fear of crime in Sweden, although the difficulty involved in measuring fear of crime is expressed in the report (Molin et al. 2019b). The survey questions range from the feeling of being unsafe while being outdoors at night, perception of crime development, concern about crime in general as well as specific crimes, to actual consequences of feeling unsafe. The last question aims to capture how behavior when moving outside of one’s own home is affected by the fear of crime. The strength of this survey lies in its ability to ask specific questions and get results over longer periods of time that can be used for comparison. The main weaknesses is that due to the size of the survey it is only performed annually, and thus cannot provide variation over shorter periods of time. There are also issues regarding response rates being differing between groups of people. Karin Dahmström (Dahmström 2011) states that surveys tend to distort the sample of respondents as those who are less proficient in the language are less likely to fill out the survey.

3. Theoretical framework and concepts In this section the theoretical framework that is used in this study is examined and explained. First, the theory of information behavior is presented. Second, the consequences of fear of crime as explained by Nicole Rader are presented. Third, the combined use of these theories for the purpose of this paper is explained.

3.1 Wilson’s model of information behavior Thomas D. Wilson coined the terms information seeking behavior and information searching behavior, which both fall under the category of human information behavior. Wilson has used these terms to describe how individuals seek information “as a consequence of a need to satisfy some goal”, and then search for information through interaction with information systems (2000).

Based on this model, the data gathered from Google Trends that is presented in this study is considered a consequence of individuals’ information seeking and information searching behavior. This implies that the seeking of information has originated from a need to satisfy a goal of any kind, related to the search query that has then been searched for, using the Google Search services. The goals that the individuals are attempting to satisfy are in this case assumed to be the acquirement of knowledge of topics that are related to fear of crime.

3.2 Consequences of fear of crime Nicole Rader (Rader 2017) explains in her paper how there are two consequences of fear of crime, psychological and social. In regards to the psychological consequences, Rader states that research has found a relationship between both anxiety as well as depression and fear of crime, where those affected report higher levels of fear of crime. As for the social consequences, they are divided into protective behaviors and avoidance behaviors. Protective behaviors include owning a weapon for the purpose of defense, or other non-weapon related protective measures such as installing a security system or extra locks. Avoidance behaviors include avoiding going out alone late at night, or visiting certain places due to fear of crime.

3.3 Change in information searching behavior as a consequence of fear of crime Combining Wilson’s model of information behavior and the theories on consequences of fear of crime presented by Rader, this paper argues that a population’s search behavior will also change as a consequence of fear of crime.

When an individual’s fear of crime changes, arguably so does its information seeking behavior. An individual will, in accordance with the theories on consequences of crime, take on either protective behaviors or avoidance behaviors. This will likely affect its information seeking behavior as the goals of the individual have changed. For example, an individual will be more likely to search online for security systems or related equipment, or search for areas with high crime rates in order to be able to avoid them. This will be measureable through Google Trends, which allows for the estimation of fear of crime using Google Trends.

4. Method This section covers why the method used is chosen. It continues to describe the course of action in implementing it, and presents its advantages and disadvantages.

4.1 Choice of method The method used in the influenza detection paper by Ginsberg et al. (2009) consisted of using correlations between the 50 million most common search queries’ frequencies on Google and the frequency of influenza-like-illness to evaluate the relevance of the final search queries used in the model. While this paper aims to estimate the level of fear of crime in Sweden based on search queries’ frequencies on Google, and uses NTU survey data as a benchmark to evaluate how well the model is able to predict this, it will use a similar method. However, where the influenza detection paper relied on correlation when selecting the search queries, this paper instead included search queries based on the questions asked in the NTU survey. In the process of choosing search queries, the wording and choice of words of the NTU survey questions have greatly been considered. This has been done in an attempt to utilize the same operationalization of fear of crime as NTU uses, with the only difference being how the data is collected. This follows the approach that has been utilized by other papers using Google Trends data, e.g. Preis et al. (2013).

4.2 Description of method The purpose of the method used in this study is to construct a way for the NTU estimations of fear of crime in Sweden to be compared to the estimations using Google Trends data. For this to be possible, a standardized relative index that can assume values from 0-100 has been created for both the NTU and Google Trends data, for all areas of observation and time periods used in this study. These areas of observations are the 21 counties of Sweden as well as countrywide level, resulting in a total of 22 areas of observation for years 2011-2019. As the NTU survey is conducted yearly the Google Trends index has been converted to its yearly average, although data is originally collected on monthly basis. This index has then been used to compare the Google Trends estimations to those of the NTU.

4.2.1 Description of NTU index Initially, the questions asked in the NTU survey are analyzed and only the questions that are constant and used throughout the time period examined in this study, 2011-2019, are included. The questions which are included in the creation of the NTU index can be found in Appendix A, while the remaining questions that were asked can be found in Appendix B along with reasons for exclusion from the NTU index.

The NTU index is a standardized relative measurement, which attain values from 0-100, and is calculated for each area of observation. This results in NTU indexes for 22 regions, namely one for each county, and one countrywide, for every year in the period observed. These indexes will be compared to the Google Trends index. The construction of the NTU index is explained in section 5.

4.2.2 Description of Google Trends index The Google Trends data is already standardized and relative at the time of collection. Google does this by calculating the amount of searches for every query of interest and dividing it by the total amount of searches made during a given time period and area of observation. The results are then used to form an index ranging from 0-100 based on all the queries compared, for a given time period and area of observation. The observation with the highest search frequency relative to all other searches made within the time period and area of observation will attain the value of 100, and all other observations will attain values relative to that. This results in a similar approach as the one when establishing an index for NTU, which allows for comparability between the two indexes. Note well that a higher index value does not necessarily indicate a higher search frequency in absolute terms, but rather relative to all other searches made.

Using the survey questions from the NTU several key words are identified as proxies for fear of crime and used as search queries in Google Trends. For example, the search query “sexual assault” [sexuella övergrepp] is based on the NTU survey question regarding going out alone during late evenings. Similarly, the search query “burglary” [inbrott] is based on the NTU survey question regarding worry of being a victim of burglary. Other search queries, such as “police” [polis] and “police station” [polisstation], are identified by the author rather than based on the poll questions from the NTU. These were included based on the perceived relevance to

12 the topics examined in the NTU. This is based on the assumption that fear of crime is correlated with, for example, how inclined an individual is to search for information about the police, or where the nearest police station is. This is supported by Wilson’s theory of information behavior, which stipulates that an individual’s search behavior is a result of a need to satisfy a goal. The goal in this case would be to acquire knowledge of the police or the police station because of fear of crime. Similarly, an increase in crime could be argued to cause an increase in both fear of crime and inclination to search for information regarding the police, whether it be contact information or something else. The search query “fraud” [bedrägeri] is included in an attempt to account for some of the fear of crime that is more related to the less violent crimes that are still included in the NTU survey question regarding the level of crime in society, or the development of the same. All search queries used are presented in Table 1 in alphabetical order.

Table 1. Search queries used as a proxy for fear of crime Search queries used Translation Bedrägeri Fraud Bilbrand Car fire Brott Crime Brottslighet Crime Hemlarm Home alarm Inbrott Burglary Misshandel Assault Otrygg Unsafe Pepparsprej* Pepper spray* Polis Police Polisstation Police station Sexuella övergrepp Sexual assault Våldtäkt Rape Överfallslarm Assault alarm Note: Table 1 shows the English translations for the search queries used, where the first “Crime” is the adjective of the word, and the second is the noun. All translations made by the author. * Topic for the search query used instead of the term, more on this in section 5.2.

The final dataset based on Google Trends consists of monthly observations for 14 queries over 22 areas of observation, for years 2011-2019, resulting in over 33,000 data points. These data

13 points will be used to create the Google Trends index, which is explained more in detail in section 5. The Google Trends index is a standardized relative measurement and is calculated for each area of observation. This results in Google Trends indexes for 22 regions, namely one for each county, and one countrywide, for every year in the period observed. These indexes will be compared to the NTU index.

4.2.3 Advantages and disadvantages with the chosen method Comparing this study’s method to that of the NTU survey, there are clear distinctions and both methods have their advantages. While the NTU survey is able to pinpoint the opinions regarding specific questions in relation to the fear of crime, such as the fear of walking alone late at night, this study does not attempt to do this as it aims to estimate fear of crime on a general level, and does not account for fluctuations in fear of specific crimes. Using a similar method as in the influenza study by Ginsberg et al. (2009), however, search queries can be identified which are highly correlated with a specific NTU survey question, which would allow for Google Trends data to estimate this as well. Moreover, where the NTU survey is only conducted once a year, the method used by this study can be utilized on a daily or even hourly basis to achieve up to date estimations for the level of fear of crime in an area of observation. This makes it possible to measure how the fear of crime is affected by seasonal variation or by a single major event, which will be further developed in section 6.

Further comparing the method used in this study to the method used in the influenza study by Ginsberg et al. (2009), it is evident that using an automated process to find the highest correlated search queries and then building a model from this is superior to a more manual method of search query selection. It could be possible that search queries which are the most semantically related to a specific NTU survey question are not the most correlated with its frequency over time. While this study is focused on approximating fear of crime in Sweden and uses NTU as a benchmark to evaluate its success, an automated process to find search queries which are highly correlated with a specific NTU survey question could have proven more effective than the manual process relying on semantic relation has been. Moreover, this could also have proven to be an effective tool in choosing search queries in order to estimate even the specific fear of crime survey questions from NTU. This is outside the scope of this study however, as it only focuses on fear of crime on a general level.

5. Data This section covers the data used in the study, and specifically how the data is used to create the Google Trends and NTU indexes.

5.1 Creation of the NTU index As the NTU survey is conducted only once annually there is one data entry for each question per year. The percentage of respondents that answered according to the definition presented in Table A1 in Appendix A are summed for each year and area of observation and divided by the maximum of the summed percentages for that area of observation, and then multiplied by 100, as follows:

∑8 푁푇푈 푁푇푈 퐼푁퐷퐸푋 = 푖=1 푖,푦푒푎푟,푐표푢푛푡푟푦−푙푒푣푒푙 × 100, (1) 푦푒푎푟,푐표푢푛푡푟푦−푙푒푣푒푙 푀퐴푋 푁푇푈푐표푢푛푡푟푦−푙푒푣푒푙

∑6 푁푇푈 푁푇푈 퐼푁퐷퐸푋 = 푖=1 푖,푦푒푎푟,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 × 100, (2) 푦푒푎푟,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 푀퐴푋 푁푇푈푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 where i denotes the individual questions. Equation (1) is used when calculating the NTU index on country-level, while equation (2) is used for the regional level. The difference in calculations is necessary as two additional survey questions were included for the country-level NTU index, but not for the regional counterpart. NTU only presented data for these two questions for years 2017-2019 on regional level, and for this reason it was excluded from the regional NTU index.

The result of these calculations is in both cases a relative index ranging from 0-100 for each year and area of observation, where the maximum for each area of observation is normalized to 100.

5.2 Creation of the Google Trends index The data available on Google Trends covers the time period from 2004 up to present day, and can be examined on country-level, or smaller regions within a country. This can then be used to compare relative popularity of search terms across regions and time. The data is normalized and scaled from 0-100, based on a search term’s popularity relative to all searches made within a given time period. A value of 0 indicates a missing value, which is caused by a very low relative search volume for a search query in a given period and area of observation. Not all searches are included in the data presented through Google Trends, but rather a sample is used to represent the entirety of searches. Repeated searches made by the same person over a short

15 period of time is also filtered out, making sure that the presented data provides the best possible estimate of the aggregated search behavior for one term, relative to other terms (Google 2019a).

Search results on both Google and Google Trends depends on how the input is formatted. The user can choose to add different operators to receive more specified results, for example using quotation marks around any search input ensures that all search results contain the words entered in a specific order (Google 2019b).

Google Trends also allows the user to search for results in two different ways. The most used, and default way, is to use what is called ‘terms’. This returns matches for all terms in a query, meaning that a search for “apple” will include results for both “apple” and “apple pie”. When including more than one word in the query the same principle applies; searching for “apple pie” will include results for “apple pie” as well as “apple for dinner”. The other way of using Google Trends is by defining search words as ‘topics’. This is a group of terms that despite language have the same concept in common. For example, a search for “Stockholm” would include results for topics such as “capital of Sweden” or “Tukholma”, which is “Stockholm” in Finnish (Google 2019c).

As the Google Trends data is by default a standardized and relative index of 0-100 based on a search terms frequency over a given time period and in a given area of observation, the search queries were compared two at a time to establish which search query had the largest absolute frequency. Based on this procedure the search query “police” [polis] was identified to have the largest absolute frequency, and all other queries were ran against it using the Google Trends search tool. This was done with “police” [polis] and four additional queries at a time, as Google Trends does not allow for more than five queries to be compared at any given time. From this point the Google Trends data was dealt with similarly to the NTU data. For each month and area of observation the values from the Google Trends output are summed and divided by the maximum value in that area of observation, and finally multiplied by 100. For year 2019 there are only 11 observations however, as values for December had not yet been presented at the point of data collection. Also, not all search queries attain a Google Trends value for all given months and areas of observation due to how Google handles low search volumes. To account for this the Google Trends index is adjusted for the amount of search queries that are included for a specific month and area of observation, indicated by the term K:

14 퐾 = ∑푗=1 푛푟 표푓 퐺표표푔푙푒 푇푟푒푛푑푠 푣푎푙푢푒 > 0푗,푚표푛푡ℎ,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛, (3) where j denotes the search query used. Finally, the yearly averages are divided by the maximum Google Trends value for the time period, 2011-2019, and multiplied by 100, as follows:

14 ∑푗=1푇푟푒푛푑푠푚,푗,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ∑12 ×100 푚=1 퐾 푀퐴푋푇푟푒푛푑푠 (( 푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ) ) 12 퐺표표푔푙푒 푇푟푒푛푑푠 푖푛푑푒푥푦푒푎푟,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 = × 100, (4) 푀퐴푋푇푟푒푛푑푠 2011−2019

( ) where m denotes the month of observation. Equation (4) is used to calculate the Google Trends index for years 2011-2018, for each area of observation. There is a maximum of 14 search queries used that are summed in the equation. If a search query attains a Google Trends value of 0 it means that it has been unable to estimate the relative search volume for the specific search query for the given time period and area of observation. The number of search queries included in a Google Trends index is adjusted for by the term K, indicating that the index is divided by the amount of individual Google Trends values that attain a value greater than zero.

14 ∑푗=1푇푟푒푛푑푠푚,푗,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ∑11 ×100 푚=1 퐾 푀퐴푋푇푟푒푛푑푠 (( 푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ) ) 11 퐺표표푔푙푒 푇푟푒푛푑푠 푖푛푑푒푥2019,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 = × 100. (5) 푀퐴푋푇푟푒푛푑푠 2011−2019

( )

Equation (5) above is used for year 2019, and differs from equation (4) in the sense that only Google Trends values for months January-November are summed, which is adjusted for in the equation.

Of the search queries that are used throughout this paper, some tend to have missing values more frequently than others. This is because of how Google handles relative volumes for search queries that are too low to maintain anonymity of the person conducting the search, in a given time period and area of observation. As this causes the search queries to contribute

17 disproportionally to the final Google Trends index used, an alternative method to construct the Google Trends index is presented. To avoid that indexes for an area of observation with many missing values are punished disproportionally for these missing values, adjustments are made to the term K. Instead of dividing by the number of queries that attain a Google Trends value greater than zero, each query’s contribution to the total sum of Google Trends values over the entire time period is calculated, and then divided by the sum of the query with the largest sum of Google Trends values. This results in different adjustments within each area of observation and time period examined, as detailed in equation (6) below:

∑2354 푇푟푒푛푑푠 퐾 = ∑14 ( 푧=1 푚,푗) , 푤ℎ푒푟푒 퐺표표푔푙푒 푇푟푒푛푑푠 푣푎푙푢푒 > 0. (6) 푎푑푗푢푠푡푒푑푧 푗=1 푀퐴푋 푇푟푒푛푑푠푇표푡푎푙

where z is the individual value for each observed Google Trends value over the entire time period, 2011-2019, with a total of 2354 observations. Using Kadjusted to account for missing values does not induce a difference in the final adjusted Google Trends index compared to the first method if all 14 search queries attain a Google Trends value that is greater than zero, as is the example of the country-level. For the smaller regions however, which to a greater extent harbor missing values, this adjustment allows for smoothening of the Google Trends index based on which search query (queries) had a missing value (values). The more missing values for a specific time period and area of observation the better the adjusted Google Trends index will perform, as it adjusts the values proportionally to their total contribution to the Google Trends index across all observations.

The adjusted Google Trends index is calculated as follows for years 2011-2018 in equation (7):

14 ∑ 푇푟푒푛푑푠 12 푗=1 푚,푗,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ∑푚=1 퐾 ×100 푎푑푗푢푠푡푒푑푧 푀퐴푋푇푟푒푛푑푠 (( 푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ) ) 퐴푑푗푢푠푡푒푑 퐺표표푔푙푒 푇푟푒푛푑푠 푖푛푑푒푥 = 12 × 100. (7) 푦푒푎푟,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 푀퐴푋 푇푟푒푛푑푠2011−2019

( )

For year 2019, the following calculation is used as depicted by equation (8) below:

14 ∑ 푇푟푒푛푑푠 11 푗=1 푚,푗,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ∑푚=1 퐾 ×100 푎푑푗푢푠푡푒푑푧 푀퐴푋푇푟푒푛푑푠 (( 푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 ) ) 퐴푑푗푢푠푡푒푑 퐺표표푔푙푒 푇푟푒푛푑푠 푖푛푑푒푥 = 11 × 100. (8) 2019,푎푟푒푎 표푓 표푏푠푒푟푣푎푡푖표푛 푀퐴푋 푇푟푒푛푑푠2011−2019

( )

The result of these calculations is in all cases a relative index ranging from 0-100 for each year and area of observation, where the maximum for each area of observation is normalized to 100.

5.3 Data characteristics In Table 2, descriptive statistics for the final NTU and Google Trends index is presented.

Table 2. Descriptive statistics for NTU and Google Trends index.

Variable Obs. Mean Standard deviation Min Max NTU index 198 87.72973 9.45207 64.99582 100 Google Trends index 198 89.23437 8.265292 61.45443 100

The two indexes display relatively similar descriptive statistics. Both are normalized with a maximum value of 100. Although all values within the range of 0-100 can be attained, none of them fall below the value of 61.

Using visualized descriptive statistics to examine the data gathered from Google Trends, several patterns appear. Figure 1 displays the average frequencies for the search queries, relative to the most popular; “police” [polis], along with standard deviation. Despite a big difference in the total number of searches the three most popular queries have very similar standard deviations. This suggests that the second and third most popular queries vary more over the time period examined.

Average relative search query frequency Unsafe 0,14 Assault alarm 0,21 Car fire 1,20 Sexual assault 1,08 Peppar spray (topic) 0,95 Crime (noun) 0,87 Police station 1,24 Home alarm 1,62 Fraud 1,69 Burglary 1,89 Assault 3,51 Rape 10,74 Crime (adjective) 11,02 Police 11,10 0 10 20 30 40 50 60 70 80 90 Figure 1. Average national search query frequency (2011-2019) relative to the most popular; “police” [police]. Standard deviation displayed for each search query.

In Figure 2 the variance of the third most popular search query; “rape” [våldtäkt] is distinguishable especially for late 2017. This could possibly be explained by the fact that this is when the Me Too movement was initiated and rapidly spread over the internet, as the topics of rape and sexual assault were increasingly reported on in the media.

300 Average relative search query frequency trend

250

200

150

100

2013-01-01 2019-09-01 2011-01-01 2011-05-01 2011-09-01 2012-01-01 2012-05-01 2012-09-01 2013-05-01 2013-09-01 2014-01-01 2014-05-01 2014-09-01 2015-01-01 2015-05-01 2015-09-01 2016-01-01 2016-05-01 2016-09-01 2017-01-01 2017-05-01 2017-09-01 2018-01-01 2018-05-01 2018-09-01 2019-01-01 2019-05-01

Police Unsafe Crime (adjective) Crime (noun) Pepper spray (topic) Assault alarm Hole alarm Police station Assault Sexual assault Fraid Rape Car fire Burglary

Figure 2. Nationwide search query frequency trend (2011-2019) relative to the most popular, “polis”

National Google Trends index trend 100 90 80 70 60 50 40 30 20 10

2012-01-01 2012-05-01 2017-05-01 2017-09-01 2011-05-01 2011-09-01 2012-09-01 2013-01-01 2013-05-01 2013-09-01 2014-01-01 2014-05-01 2014-09-01 2015-01-01 2015-05-01 2015-09-01 2016-01-01 2016-05-01 2016-09-01 2017-01-01 2018-01-01 2018-05-01 2018-09-01 2019-01-01 2019-05-01 2019-09-01 2011-01-01 Figure 3. National trend of fear of crime-index for time period 2011-2019.

As for the national Google Trends index trend displayed in Figure 3, it appears to contain a positive time trend. The peak observation of late 2017 also coincides with the high frequency of the search query “rape” [våldtäkt] seen in Figure 2. Figure 3 also appears to display a seasonal variation. Both the time trend and seasonal variation observable in Figure 3 will be further developed on in section 6.3.

6. Empirical strategy and results This section is divided into three parts. First, the Google Trends fear of crime-index is compared to the NTU index. Second, data on fear of crime during an exogenous event is presented. Lastly, the potential of Google Trends data compared to the NTU survey-data is covered.

6.1 Fit of Google Trends index To evaluate the performance of the Google Trends index in estimating fear of crime it is compared to the NTU index, and the Pearson correlation coefficient is calculated. This is done for each area of observation. Figure 4 presents the results for country-level.

Figure 4. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Sweden for period 2011-2019. Correlation: 0,8384, significance level: 0,0048

From 2013 and onwards the Google Trends index is able to accurately estimate the NTU index, producing a statistically significant correlation coefficient of 0,8384 for the time period examined. The results for country level are the same regardless of if the adjusted Google Trends index is used or not, as all 14 search queries had a trends value greater than zero for all periods of observation. For regional level however, the results differ. For Stockholm county, the correlation coefficients are 0,8924 and 0,8893 for the non-adjusted and adjusted Google Trends index respectively. Correlations for both Sweden and Stockholm county all have a significance level of <1 %. For Jönköping County, the correlation coefficients are -0,7584 (significance level <5 %) and -0,54882 (non-significant result) respectively. Correlations for NTU and Google Trends index as well as its adjusted counterpart for all regions can be found in Appendix C and D, respectively.

6.2 Information searching behavior during an exogenous shock To determine if fear of crime is related to the distance from a criminal act, the 2017 events at Drottninggatan in Stockholm are used as an exogenous shock to determine if it had any effect on information searching behavior. The Google Trends-value for search terms related to the terror attack are regressed on the distance from the place where the events occurred. The Google Trends-values are measured on county-level, and the distance from the events are calculated as the distance from Drottninggatan, Stockholm to the capital of each county, if traveling by car as measured by Google Maps.

The following equation is used to estimate this effect:

퐺표표푔푙푒 푇푟푒푛푑푠 푣푎푙푢푒푖 = 훽0 + 훽1퐷푖푠푡푎푛푐푒푖 + 휀푖 (9)

where the outcome variable Google Trends valuei indicates the Google Trends values for the topic related to the 2017 Stockholm terrorist attacks [terrordådet i Stockholm 2017] for time period April 6-12 2017, and Distancei indicates the geographical distance in km for county i from Drottninggatan, Stockholm. εit is the error term. There are no control variables included in equation (9), following a similar approach to that of Choi et al. (2012). Below, equation (9) is regressed through Ordinary Least Squares.

Table 3. OLS-regression of Google Trends topic on distance from Drottninggatan, Stockholm VARIABLES Google Trends value

Distance -0.029** (0.013) Constant 82.309*** (4.540)

Observations 17 R-squared 0.366 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 3 displays the estimates of the effect of being an additional km away from Drottninggatan in Stockholm during April 6-12 2017 on information searching behavior regarding the 2017

Stockholm terrorist attacks. Column one shows the estimate of β1 from equation (9). The estimate is interpreted as a change of -0,029 in the Google Trends value for the topic per

23 kilometer further away from Drottninggatan a search is made. This indicates that individuals were less inclined to search for information on the 2017 Drottninggatan events as their distance to the events increased. This implies that the change in information search behavior as a consequence of fear of crime diminishes as individuals tend to pay less regard to events that have occurred far away.

6.3 Potential of Google Trends data in estimating fear of crime Taking advantage of the fact that the Google Trends data used for this paper has monthly observations on the Google Trends index whereas the NTU index has observations on yearly basis, both indexes are tested for seasonal variation and time trends.

The following equations are used to determine if the indexes contain seasonal variation:

퐼푛푑푒푥푉푎푙푢푒푖푡 = 훽0 + 훽1푄2푖푡 + 훽2푄3푖푡 + 훽3푄4푖푡 + 휀푖푡, (10)

퐼푛푑푒푥푉푎푙푢푒푖푡 = 훽0 + 훽1푊푖푛푡푒푟푖푡 + 휀푖푡, (11)

where the outcome variable IndexValuei indicates the value for the NTU or Google Trends indexes, and the regressors Q2i, Q3i, and Q4i are dummy variables taking on the value of 1 if the observation of the index is done in quarter two, three, or four, respectively, for index i and time period t. Q1i, the first quarter, is excluded and serves as the reference group. The dummy variable Winteri takes on the value of 1 if the observation of the index is done in either quarter one or four, during the winter months.

To determine if the indexes contain time trends, the following equation is used:

퐼푛푑푒푥푉푎푙푢푒푖푡 = 훽0 + 훽1푇푖푚푒푖푡 + 휀푖푡, (12)

where the variable Timeit attains a value for each monthly observation, for t=(1,2,3…107) for all 107 observations. Below, OLS-regressions of equation (10) through (12) on national level are presented in Table 4.

Table 4. OLS-regressions of national NTU and Google Trends index on time variables. NTU index Google Trends index VARIABLES (1) (2) (3) (4) (5) (6)

Q2 0.000 -5.699** (2.069) (2.844) Q3 0.000 -2.254 (2.069) (3.181) Q4 -0.321 6.893** (2.082) (3.444) Winter -0.157 7.358*** (1.454) (2.273) Time 0.205*** 0.324*** (0.010) (0.022) Constant 90.410*** 90.410*** 79.286*** 68.128*** 64.152*** 50.313*** (1.463) (1.024) (0.768) (2.246) (1.431) (1.146)

Observations 107 107 107 107 107 107 R-squared 0.000 0.000 0.720 0.141 0.091 0.673 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 3 displays the estimates from OLS-regressions of NTU and Google Trends index on time variables. Both indexes are regressed separately for each model specification.

Column one and four in Table 4 display the estimates for the coefficients β1, β2, and β3 from equation (10), for the NTU and Google Trends index respectively. They are interpreted as the average difference in the indexes when comparing the second, third, and fourth quarter to the first quarter, respectively. None of the coefficients for the seasonal variation for the NTU index display significant results. This was expected, as this index is measured on yearly basis, and thus cannot contain seasonal variation. With regards to the coefficients for the seasonal variation for the Google Trends index found in column four however, Q2 and Q4, displays significant results. The negative value for Q2 indicates that the Google Trends index is associated with values that are ~5,7 index points lower for the second quarter compared to the first one. The positive value for Q4 on the other hands indicates that the Google Trends index is associated with values that are ~6,9 index points higher for the fourth quarter compared to the first one. Given these results, indications of seasonal variation in the Google Trends data is identified.

Column two and five in Table 4 display the estimates for the β1 coefficient from equation (11), for the NTU and Google Trends index respectively. Again, the coefficient for the NTU index does not display significant results, as expected. For the Google Trends index, the positive coefficient for Winter indicates that the months October-March are associated with values for the Google Trends index that are ~7,3 index points higher compared to the other half of the year. The applications to fear of crime is thus that people generally exhibit higher levels of fear of crime in Sweden during the winter period, during 2011-2019. This could possibly be explained by the fact that Sweden experiences less sunlight during the winter period. A study from Great Britain (Atkins et al. 1991) showed that while improved public lighting does not reduce crime in an area, it does improve the residents feeling of safety, thus helping to reduce fear of crime.

Column three and six in Table 4 display the estimates for the β1 coefficient from equation (12), for the NTU and Google Trends index respectively. The coefficients display significant results for both indexes. They are both positive, indicating that fear of crime has been on the rise during 2011-2019. The coefficient in column three is interpreted as a monthly increase in the NTU index by ~0,2, while the coefficient in column six is interpreted as a monthly increase in the Google Trends index by ~0,3. These results are in accordance with the results published in the NTU report of 2019, as their statistics show rising trends for several survey questions related to fear of crime during 2011-2019 (Molin et al. 2019b).

7. Discussion and concluding remarks The results from the comparison of the Google Trends and NTU index in this paper show that data from Google Trends can be used to estimate fear of crime for larger regions and country- level in Sweden. Furthermore, this paper has shown that there are several advantages to using Google Trends data over survey data, which is the most commonly used method to estimate fear of crime. Where NTU only produces yearly estimates, the Google Trends index can be produced with estimates updated daily. It has also proven to be able to ascertain that fear of crime has a seasonal variance component to it, where people tend to be more worried during the winter season. What can also be taken from the results of this paper is the effect that fear of crime has on information searching behavior, which has previously not been discussed in the literature.

This paper has provided the previously survey-focused field of study of fear of crime with a new method, utilizing Google Trends data. Whereas this paper has estimated fear of crime on a general level, future research should focus on estimating fear of crime on a more specific level. Moreover, if more frequent data on fear of crime was made available it would be possible to further evaluate the performance of Google Trends as a way to estimate fear of crime.

References Articles

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Appendix A: Questions from The Swedish Crime Survey included in NTU index

Here the questions that are included in the NTU index are presented in their original design, along with which results are included as well as translations.

Table A1. NTU survey questions included in the NTU index, presented with Brå’s definition of which results are reported. Original questions Translated questions

Oro över Resultat avser de som (Feeling of) worry Results refer to those who brottsligheten i upplever oro i stor about crime in experience worry to a great samhället utsträckning* society extent* Otrygghet vid Resultat avser de som Feeling unsafe Results refer to those who utevistelse sent på upplever att de är while outside late at experience being kvällen i det egna mycket/ganska otrygga night in area of very/moderately unsafe bostadsområdet residence Valt annan väg eller Resultat avser de som Chose another way Results refer to those who färdsätt** upplever att de mycket/ganska or means of experience that they ofta valt en annan väg eller ett transportation** very/moderately often choose annat färdsätt på grund av oro another way or other means of för att utsättas för brott transportation due to fear of being a victim of crime Avstått från Resultat avser de som Refrained from Results refer to those who aktivitet** mycket/ganska ofta har avstått partaking in very/moderately often have från någon aktivitet på grund activity** refrained from partaking in an av oro för att utsättas för brott activity due to worry of being a victim of crime Oro för att utsättas Resultat avser de som Worry of being a Results refer to those who för bostadsinbrott mycket/ganska ofta oroar sig vicitim of burglary very/moderately often worry för att utsättas för over being a victim of bostadsinbrott burglary Oro för att utsättas Resultat avser de som Worry of being a Results refer to those who för stöld mycket/ganska ofta oroar sig victim of car theft very/moderately often worry av/skadegörelse på för att utsättas för stöld av bil or vandalisation over having their car stolen or bil eller skadegörelse på bil vandalized Note: * Answers “not sure” and “don’t know” excluded from results. ** Only included on country-level. All translations made by the author.

Table A1, continued. NTU survey questions included in the NTU index, presented with Brå’s definition of which results are reported.

Original questions Translated questions

Oro för att Resultat avser de som Worry that Results refer to those who närstående skall mycket/ganska ofta oroar sig someone close to very/moderately often worry drabbas av brott för att någon närstående ska you will be a victim that someone close to them drabbas av brott* of crime will be a victim of crime Uppfattningen om Resultat avser de som tror att Perception of crime Results refer to those who brottsutvecklingen i brottsutvecklingen ökat development in believe that crime Sverige kraftigt/något de senaste tre Sweden development has increased åren substantially or slightly in the last three years Note: * Answers “not sure” and “don’t know” excluded from results. ** Only included on country-level. All translations made by the author.

Appendix B: Questions from The Swedish Crime Survey

The questions asked in the physical mail-version of the NTU survey differed from the ones asked in the online version, which featured nine more questions in addition to the ones asked in the physical mail-version. The questions asked are presented in their original design along with an English translation, as well as information on whether they were included in the NTU index or not and the reason for exclusion, if applicable.

Table B1. NTU survey questions in the physical survey.

Included Reason in the for NTU exclusion Original questions Translated questions index by from the NTU author index (Y/N) I vilken utsträckning är du orolig To what extent do you worry about Y över brottsligheten I samhället? crime in society? Om du går ut ensam sent en kväll i If you go out alone in the area Y området där du bor, hur trygg eller where you live during late otrygg känner du dig då? evenings, how safe or unsafe do you feel? Har det hänt under det senaste året During the last year, have you Y * att du valt att ta en annan väg eller ett chosen another way or another annat färdsätt på grund av oro för att means of travel because of fear of utsättas för brott? being a victim of crime? Har det hänt under det senaste året During the last year, have you Y * att du avstått från någon aktivitet, refrained from any activity, for t.ex. gå på promenad, restaurang example taking a walk, visiting a eller träffa någon på grund av oro för restaurant or meeting a friend, att utsättas för brott? because of fear of being a victim of crime? Note: * Included on country-level, but not on county-level due to the questions being merged for period 2011- 2016, and presented individually for period 2017-2019 in such a way there they were impossible to compare. ** Excluded due to data not being available for period 2011-2016. All translations made by the author.

Table B1, continued. NTU survey questions in the physical survey.

Included Reason in the for NTU exclusion Original questions Translated questions index by from the NTU author index (Y/N) Har det hänt under det senaste året During the last year, have you N ** att du har avstått från att skriva något refrained from writing or posting eller lägga upp bilder eller filmer på photos or videos on the internet, internet, på grund av oro för att bli because of fear of being a victim of utsatt för trakasserier eller hot? harassment or threats? Har det hänt under det senaste året During the last year, have you Y att du oroat dig för att du ska drabbas worried about being a victim of av inbrott i din bostad? burglary in your residence? Har det hänt under det senaste året During the last year, have you Y att du oroat dig för att din/er bil ska worried that your car could be bli stulen eller utsatt för stolen or subject to vandalisation? skadegörelse? Har det hänt under det senaste året During the last year, have you N ** att du oroat dig för att bli utsatt för worried about being a victim of bedrägeri om du köper varor eller fraud while buying goods or tjänster på internet? services on the internet? Har det hänt under det senaste året During the last year, have you N ** att du oroat dig för att du ska bli worried about being the victim of misshandlad? assault? Har det hänt under det senaste året During the last year, have you N ** att du oroat dig för att du ska bli worried about being a victim of rånad? robbery? Note: * Included on country-level, but not on county-level due to the questions being merged for period 2011- 2016, and presented individually for period 2017-2019 in such a way there they were impossible to compare. ** Excluded due to data not being available for period 2011-2016. All translations made by the author.

Table B1, continued. NTU survey questions in the physical survey.

Included Reason in the for NTU exclusion Original questions Translated questions index by from the NTU author index (Y/N) Har det hänt under det senaste året During the last year, have you N ** att du oroat dig för att du ska bli worried about being a victim of våldtagen eller på annat sätt sexuellt rape or in other ways sexually angripen? assaulted? Har det hänt under det senaste året During the last year, have you Y att du oroat dig för att någon worried about someone close to you närstående till dig ska drabbas av being a victim of crime? brott? I vilken utsträckning påverkar oro för To what extent does worry of being N ** att utsättas för brott din livskvalitet? a victim of crime affect your quality of life? I vilken utsträckning tycker du att In your opinion, to what extent does N ** polisen bryr sig om problem som the police care about the problems finns i området där du bor? in your area? På det hela taget, tror du att antalet Overall, do you think that the Y brott i Sverige har ökat, minskat eller number of crimes in Sweden has varit oförändrat de senaste tre åren? increased, decreased or remained unchanged during the last three years? Note: * Included on country-level, but not on county-level due to the questions being merged for period 2011- 2016, and presented individually for period 2017-2019 in such a way there they were impossible to compare. ** Excluded due to data not being available for period 2011-2016. All translations made by the author.

Source: Molin et al. 2019c.

Table B2. Questions asked in the online version, in addition to the ones also asked in the physical version.

Included Reason in the for NTU exclusion Original questions Translated questions index by from the NTU author index (Y/N) I vilken utsträckning upplever du att To what extent do you consider the följande är ett problem i området där following to be a problem in the du bor? area where you live? Nedskräpning Littering N ** Skadegörelse Vandalisation N ** Klotter Graffiti N ** Fortkörning Speeding N ** Övrig störande körning med moped, Other disturbances related to the N ** bil eller andra motorfordon driving of mopeds, cars or other motor vehicles Personer påverkade av alkohol eller People under the influence of N ** droger utomhus alcohol or drugs outside Gäng som uppehåller sig i området Gangs residing in the area N ** Personer eller gäng som bråkar eller People or gangs who start fights or N ** stör cause disturbances Öppen narkotikahandel Drugs sold in ”broad daylight” N ** Note: “Broad daylight” implies that the seller does not take measures to hide the activity. ** Excluded due to data not being available for period 2011-2016. All translations made by the author.

Source: Molin et al. 2019c.

Appendix C: Google Trends index

Graphs and correlations on county level containing Google Trends-index of fear of crime fitted with NTU index of fear of crime for period 2011-2019. Presented with time (year) on X-axis and normalized relative indexes of Google Trends and NTU on Y-axis, with the scale going from 55-105.

Sweden

Figure C1. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Sweden for period 2011-2019. Correlation: 0,8384, significance level: 0,0048

Stockholm County

Figure C2. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Stockholm County for period 2011-2019. Correlation: 0,8924, significance level: 0,0012

Uppsala County

Figure C3. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Uppsala County for period 2011-2019. Correlation: 0,536, significance level: 0,1368

Södermanland County

Figure C4. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Södermanland County for period 2011-2019. Correlation: -0,7636, significance level: 0,0166

Östergötland County

Figure C5. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Östergötland County for period 2011-2019. Correlation: 0,6484, significance level: 0,0589

Jönköping County

Figure C6. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Jönköping County for period 2011-2019. Correlation: -0,7584, significance level: 0,0178

Kronoberg County

Figure C7. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Kronoberg County for period 2011-2019. Correlation: 0,6019, significance level: 0,0864

Kalmar County

Figure C8. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Kalmar County for period 2011-2019. Correlation: -0,3335, significance level: 0,3804

Gotland County

Figure C9. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Gotland County for period 2011-2019. Correlation: -0,8977, significance level: 0,0010

Blekinge County

Figure C10. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Blekinge County for period 2011-2019. Correlation: -0,8361, significance level: 0,0050

Skåne County

Figure C11. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Skåne County for period 2011-2019. Correlation: 0,4823, significance level: 0,1886

Halland County

Figure C12. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Halland County for period 2011-2019. Correlation: -0,8809, significance level: 0,0017

Västra Götaland County

Figure C13. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västra Götaland County for period 2011-2019. Correlation: 0,7746, significance level: 0,0143

Värmland County

Figure C14. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Värmland County for period 2011-2019. Correlation: -0,1531, significance level: 0,6942

Örebro County

Figure C15. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Örebro County for period 2011-2019. Correlation: -0,5445, significance level: 0,1296

Västmanland County

Figure C16. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västmanland County for period 2011-2019. Correlation: -0,1134, significance level: 0,7715

Dalarna County

Figure C17. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Dalarna County for period 2011-2019. Correlation: -0,4615, significance level: 0,2111

Gävleborg County

Figure C18. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Gävleborg County for period 2011-2019. Correlation: -0,3522, significance level: 0,3527

Västernorrland County

Figure C19. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västernorrland County for period 2011-2019. Correlation: -0,8300, significance level: 0,0056

Jämtland County

Figure C20. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Jämtland County for period 2011-2019. Correlation: -0,6859, significance level: 0,0413

Västerbotten County

Figure C21. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västerbotten County for period 2011-2019. Correlation: -0,1044, significance level: 0,7892

Norrbotten County

Figure C22. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Norrbotten County for period 2011-2019. Correlation: -0,3354, significance level: 0,3776

Appendix D: Adjusted Google Trends index Graphs and correlations on county level containing adjusted Google Trends-index of fear of crime fitted with NTU index of fear of crime for period 2011-2019. Presented with time (year) on X-axis and adjusted normalized relative index of Google Trends and NTU index on Y-axis, with the scale going from 55-105.

Sweden

Figure D1. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Sweden for period 2011-2019. Correlation: 0,8384, significance level: 0,0048

Stockholm County

Figure D2. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Stockholm County for period 2011-2019. Correlation: 0,8893, significance level: 0,0013

Uppsala County

Figure D3. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Uppsala County for period 2011-2019. Correlation: 0,6636, significance level: 0,0513

Södermanland County

Figure D4. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Södermanland County for period 2011-2019. Correlation: -0,6662, significance level: 0,0501

Östergötland County

Figure D5. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Östergötland County for period 2011-2019. Correlation: 0,8690, significance level: 0,0024

Jönköping County

Figure D6. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Jönköping County for period 2011-2019. Correlation: -0,5482, significance level: 0,1265

Kronoberg County

Figure D7. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Kronoberg County for period 2011-2019. Correlation: -0,1095, significance level: 0,7791

Kalmar County

Figure D8. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Kalmar County for period 2011-2019. Correlation: -0,1399, significance level: 0,7196

Gotland County

Figure D9. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Gotland County for period 2011-2019. Correlation: -0,8836, significance level: 0,0016

Blekinge County

Figure D10. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Blekinge County for period 2011-2019. Correlation: -0,2350, significance level: 0,5428

Skåne County

Figure D11. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Skåne County for period 2011-2019. Correlation: 0,6525, significance level: 0,0568

Halland County

Figure D12. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Halland County for period 2011-2019. Correlation: -0,6825, significance level: 0,0428

Västra Götaland County

Figure D13. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västra Götaland County for period 2011-2019. Correlation: 0,7765, significance level: 0,0139

Värmland County

Figure D14. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Värmland County for period 2011-2019. Correlation: 0,2982, significance level: 0,4357

Örebro County

Figure D15. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Örebro County for period 2011-2019. Correlation: -0,3093, significance level: 0,4181

Västmanland County

Figure D16. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västmanland County for period 2011-2019. Correlation: 0,2748, significance level: 0,4743

Dalarna County

Figure D17. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Dalarna County for period 2011-2019. Correlation: -0,2277, significance level: 0,5557

Gävleborg County

Figure D18. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Gävleborg County for period 2011-2019. Correlation: -0,4026, significance level: 0,2826

Västernorrland County

Figure D19. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västernorrland County for period 2011-2019. Correlation: -0,7117, significance level: 0,0315

Jämtland County

Figure D20. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Jämtland County for period 2011-2019. Correlation: -0,6781, significance level: 0,0447

Västerbotten County

Figure D21. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Västerbotten County for period 2011-2019. Correlation: -0,3767, significance level: 0,3176

Norrbotten County

Figure D22. Graph of Google Trends-index of fear of crime and NTU index of fear of crime for Norrbotten County for period 2011-2019. Correlation: 0,0815, significance level: 0,8349