The Influence of Aggression and Depression on the Color Preference of Artists across the US

Ross Hattel

Metropolitan State University of Denver

May 8, 2019

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Abstract:

The following study utilizes geospatial methods to investigate whether psychological traits of

depression and aggression can be correlated with the color preferences of local artists across the United

States. Several studies from the field of color psychology have shown that colors such as grey and black

are more often used to describe depressed moods than brighter colors. Red has also been shown to be

correlated with interpersonal hostility and aggression. The study uses county suicide rates as a proxy for

depression and county homicide rates as a proxy for aggression. This study provides for a unique line of

investigation to highlight the wide range of study areas that geospatial sciences has. Another major goal

of the study, was to investigate whether geospatially based mental health trends could be correlated

with individual mental health states.

Introduction:

The question is often asked, what is art? Art is commonly thought of as a method of human

expression through a variety of different creative endeavors. Art is as varied as it is controversial and

one person’s perception of what art is, may be completely different from another’s. The fact that art is

an expression of human creativity, and thus is an expression of the mind, leads to obvious connections

between art and the field of psychology. Two important questions to ask are, can certain characteristics

of an artwork reveal anything about a person’s mental state, and can these characteristics be identified

on a large scale? The large scale question implies a geospatial perspective as a means for correlating art

composition with psychological traits and location. Using geospatial sciences to investigate art and

psychology is a unique method for investigation but provides for an interesting challenge.

Famous artists have often been popular not only for their masterful works but also for their

eccentric and bizarre behavior. Vincent Van Gogh was purported to suffer from psychotic episodes

which eventually led him to sever part of his ear with a razor. Louis Wain began his career painting

pictures of ordinary housecats, but as he slipped further into the throes of schizophrenia, his cat

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paintings became increasingly distorted (Stefano & Fusar-Poli, 2018). In his later years, his cat paintings

became a kaleidoscope of geometric shapes, vaguely resembling cats. Wain’s case gives a striking

example of how a person’s mental state can influence the content of creative expression.

The links between creative expression and mental states are also evident in the field of art

therapy, which utilizes creative expression as a tool for dealing with mental health issues. Some studies

have suggested that art therapy is more effective for depressive symptoms caused by terminal illness

and lack of mobility than either talk therapy or pharmacology (Blomdahl, Gunnarsson, Guregard, &

Bjorklund, 2013, p. 323). The mechanism of action is not understood, however. The subject matter and

content of paintings by people with mental illnesses vary widely and an analysis of themes would

produce a multitude of results. At a more basic level, an analysis of color could produce a quantitative

rather than qualitative analysis of a work of art (specifically a painting or other media on a surface).

Calculating color proportions along with different levels of saturation, would produce tangible numbers

that are easier to analyze and less subjective. A quantitative analysis could then allow for a study to be

carried out on a broad geographic scale.

Multiple studies have been conducted through the years that have attempted to determine if

there is a link between color preference and psychiatric disorders. Holmes, Fouty and Wurtz (1985)

conducted a study in 1985, that tested the color preference of 1,143 psychiatric patients, using the

Luscher Color Test (Holmes, Fouty, & Wurtz, 1985, p. 747). The results showed that female patients

tended to pick yellow and red, while males preferred blue. Unfortunately, the results were somewhat

inconclusive given that the differences between color rankings only differed by a few individuals. The

study also did not delineate between mental disorders and instead generalized to all psychiatric

patients. Another study conducted by Tao, Xu Pan, Gao, and Wang (2015), sought to determine the link

between color preference and personality traits in schizophrenic patients. This study determined that

schizophrenic patients were more likely to choose brown over green as their preferred color.

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Schizophrenics exhibiting neurotic-anxious personality traits were most likely to prefer black (Tao, Xu,

Pan, Gao, & Wang, 2015, p. 175).

There have also been some more robust studies that have focused on color preference and

personality, as well as mental health issues such as anxiety and depression. Carruthers, Morris, Tarrier,

and Whorwell (2010) conducted a rigorous study with healthy, depressed, and anxious participants. The

researchers used red, orange, yellow, green, blue, black, white, brown, pink, purple and 4 different

shades of grey as the color choices. Each color was also presented with different saturation levels to

account for the fact that different shades could have different connotations. Participants were asked 3

questions; “Which color are you most drawn to? Which color is your favorite? Which color describes

your mood?” (Carruthers, Morris, Tarrier, & Whorwell, 2010, p. 3) Colors were also rated as either

positive or negative and placed into 8 different permutations for participants to choose from. The results

of the study indicated that depressed and anxious individuals were more likely to choose different

shades of grey and colors with less saturation to describe their moods. Blue was the most favored color

overall and yellow was the color most chosen by healthy volunteers.

Another impressive study by Fetterman, Liu, and Robinson (2014), examined how interpersonal

hostility related to preferences for the color red. The researchers sought to determine whether

interpersonal hostility led to a preference for red over blue, a bias to perceive red over other colors in a

computer aided perceptual task, and whether a preference for red was associated with hostile decision

making. A test known as the warmth-coldness dimension of revised interpersonal adjective scale was

used to determine a person’s level of interpersonal bias. Hostile decision making was assessed using

“five different moral dilemma questions” (Fetterman, Liu, & Robinson, 2015, p. 111). Overall red was

associated with interpersonal hostility in all three conditions.

The previous two studies mentioned, establish a correlation between certain color preferences,

personality, and certain mental disorders. Depressed and anxious individuals are more likely to prefer

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grey and less saturated colors, while a preference for red indicates a tendency towards interpersonal

hostility. An interesting line of investigation is to see whether these types of color preferences are

noticeable on a geographic scale.

Utilizing geographic information systems for mental health studies is a testament to the wide-

ranging applications of geospatial sciences. In the past, geographic studies have linked high levels of

psychiatric hospitalization with low socioeconomic status (Hudson, 2012, p. 108). Others have revealed

a correlation between increased rates of schizophrenia and inner-city neighborhoods (Hudson, 2012, p.

108). A more recent mental health study involving GIS, focused on how socioeconomic factors and the

built environment influence well-being. Nordbo, Nordh, Raanaas, and Aamodt’s 2018 study, showed

that factors such as “mixed land-use areas, access to recreational and commercial areas, neighborhood

green space, proximity to green spaces, safe neighborhoods, and diverse walking areas” are associated

with increased well-being, which is an important indicator for mental health Nordbo, Nordh, Raanaas, &

Aamodt’s, 2018, p. 20).

There are many challenges to the study of mental health through GIS. Mental health is a

complex issue with many environmental, genetic, social, economic, political, and behavioral

determinants. GIS data is often aggregated over large areas and there is a large amount of

generalization that would occur for a complex process with many individual determinants. This falls into

the realm of the “ecological fallacy” (Bell, 2009, p. 170). Other problems that are typical of GIS also

occur as well, such as the modifiable area unit problem (Bell, 2009, p.170). The different geographical

units with which a researcher uses to convey data could lead to vastly different results.

Geospatial sciences can play an important role in the study of mental health issues. It can help

to determine if there are geographic disparities in the quality of mental health care as well as establish

different trends in mental health across space and time. Studies have shown that mental health can be

correlated with socioeconomic disparities as well as with access to recreational and mixed land use

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areas. The myriad ways in which GIS has been used in the past in the field of psychology give credence

to the idea that it could potentially be used in a subject as obscure as color psychology. Color psychology

could be used to draw correlations between mental states of anxiety, depression, and interpersonal

hostility with certain color preferences in local art from across the United States. Geographic data is

often broad in scope, so this type of research could be useful in determining if certain psychological

trends in a given geographic region, have impacts on the mental states of local artists as well.

Purpose:

The main purpose of this study is to determine whether color preference in local art from cities around

the country can be correlated with psychological traits of aggression and depression. The first objective

of this study is to determine whether the color grey, black, or white is more prevalent in the artwork of

people who are depressed. Some studies have indicated that depressed individuals are more likely to

use grey to describe their mood. Depression is correlated with higher rates of suicide and suicide rates

are used as a proxy for depression rates in this study. A second major objective is to determine if a

preference for red is related to higher rates of hostility and aggression. Homicide rates are used as a

proxy for hostility and aggression because homicides are correlated with traits of interpersonal hostility.

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Datasets:

Name: Data Type: Description: Source: Status:

cb_2017_us_ua10_500k.shp shapefile Urban areas in the US

US Census Bureau

Available onlineProjected into

project geodatabase

cb_2017_us_state_500k.shp shapefile State boundaries

US Census Bureau

Available onlineProjected into

project geodatabase

cb_2017_us_county_500k.shp shapefile County boundaries

US Census Bureau

Available onlineProjected into

project geodatabase

Suicide_Rates_US_1 Txt document Intentional self-harm deaths by

county (crude rate)

CDC: Wonder Database

Available onlineJoined to county

shapefile

Homicide_Rates_US_1 Txt document A table of crude

homicide rates from 2000 to

2017

CDC: Wonder Database

Available OnlineJoined to county

shapefile

ColorAnalyzerData CSV file Color proportions by

percent average from

14 cities

Google,TinEye Labs

Created in Excel and joined to city

shapefile

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Methods:

The first phase of this study involved gathering datasets and other applications for processing

the data. Three primary datasets for linking the variables together were found on the US Census Bureau

website. These included city, county, and state shapefiles. These shapefiles were imported into a

primary data folder on a USB device. Next, datasets that could be used as a proxy for aggression and

depression were researched using Google. The CDC Wonder database was used for these datasets

because they could be tied to counties. Using the table generator, two text documents representing

crude suicide rates and crude homicide rates were created. These two text documents were converted

into an excel table and then imported into the primary data file.

The second step in the data gathering process, was searching for a sufficient color analyzer that

would summarize digital images based on color proportions. A website from TinEye Labs summarized

the data based on proportions and provided a masking for irrelevant background colors. Next, a Google

search was conducted to select artwork from across the United States. Two websites were found that

allowed for artwork to be searched by city. Streetartcities.com was the primary website used and a

secondary website, Wescover.com was used if Street Art Cities yielded few results. Street Art Cities uses

a web map to catalog different street art murals in cities across the country. Cities were selected from

east to west, trying to remain as random as possible. Artist names were searched on google to ensure

that the artist was from the city listed. Each image with artist name was then snipped into a word

document and imported into the art data folder on a USB device. The word document was organized by

city.

In order to run the color analysis on the artwork, an excel spreadsheet was created that listed

each city along with the names of 5 different artists selected from each city. Each piece of art was edited

in Microsoft Paint to prepare them for the color analysis. If the picture was taken at too much of an

angle, an alternate photo or work of art from the artist was searched on Google. The areas that were

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not included as part of the artwork, such as background walls were cut out of the picture. Each image

was then uploaded to the color summarizer on the TinEye website. The color summarizer listed colors

contained in each image along with their percentages. The percentage of each color was then entered

into the artwork excel spreadsheet. There was an element of subjectivity as some of the colors did not

have a resemblance to the label placed on them. In these cases, the colors were interpreted through

human eyes. The percentages from each color for each city were then averaged and copied into another

excel table. This table, known as the color percentage averages table, contains the average percentage

of all colors for each city.

After collecting the data, an advanced project geodatabase was created in a derived data folder

where the city, county, and state shapefiles were projected into an Albers equal area USA contiguous

projection. The county code field was used to link the county shapefile with both the homicide and

suicide rate excel tables previously created. A new county code field called Cou_code had to be created

in the county shapefile in order to match the county code data type of the homicide and suicide rate

tables. The old county code field was recalculated as numeric in the newly created Cou_code field. A

table join was then used to join the homicide and suicide rate tables to the counties shapefile as seen in

figure 1. At this point the county table had all the county information along with suicide and homicide

rate data appended it. This table was then converted to a feature class in the Advanced Project

geodatabase and named the Co_Homicide_Suicide layer.

Figure 1: Diagram of model used in joining county shapefiles with homicide/suicide data.

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The next step in the process involved selecting the proper cities from the cities layer and then

joining those cities with the previously created Co_Homicide_Suicide layer. A select features function

was used to create a layer with all 14 cities used in the study. This layer was named Cities_Select_1. This

layer was then used to select all the counties that each city crossed with a select layer by location

function. Each county was then inspected and manually removed if it was barely crossed by a city

boundary. A new layer called SelectedCounties was created from this selection as seen at the top of

Figure 2.

The color percentage averages table created in excel from the color summarizer, was then

joined with the Cities_Selec_1 layer using the city name as the join field. With this new table, a spatial

join was performed with the SelectedCounties layer. This layer was named Color_H_S_SpatialJoin and

had all relevant data for the study joined into one table and shapefile. During the spatial join the total

suicide, homicide, and overall population were summed from each county. A new homicide rate and

suicide rate field were added to aggregate the data from the counties that intersected each city. Each of

these fields was then calculated using the formula: (Suicide/Homicide) Deaths / Total Population

*100,000. Finally, this table was converted to a feature class and named HSColorJoin as seen at the far

right of Figure 2.

Figure 2: Diagram of model used in joining cities with color data and underlying counties.

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After completing the final HSColorJoin layer, the file was imported into Excel for the final

analysis. A linear regression was performed using the statistical analysis tools within Excel in order to

discover any positive or negative correlations between homicide and suicide rates and colors. Maps

were also created using proportional symbols for the homicide/suicide rates and bar graphs indicating

color proportions from each city.

Results:

The linear regression analysis for crude homicide rates was statistically significant overall with

an f-score of 0.010966088. Surprisingly the coefficient for red was only 0.437481822, which was on the

lower end of the positive correlation. The p-value for red was also less statistically significant with a

score of 0.16856737. The color with the highest positive correlation was yellow with a coefficient of

0.766976036. The p-value for yellow was 0.045134213 which is within the range of statistical

significance. Blue had the lowest p-value at 0.030928603, with a coefficient of 0.656573637 giving it a

high statistical significance and a high positive correlation.

Table 1: homicide rate and color analysisSUMMARY OUTPUT

Regression StatisticsMultiple R 0.994182157R Square 0.988398161Adjusted R Square 0.949725364Standard Error 0.580027377Observations 14

ANOVAdf SS MS F Significance F

Regression 10 85.9851263 8.59851263 25.55796957 0.010966088Residual 3 1.009295274 0.336431758Total 13 86.99442157

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept -47.09342518 21.37756206 -2.202937129 0.11485633 -115.1263686 20.9395182 -115.1263686 20.9395182Red 0.437481822 0.242161201 1.806572733 0.16856737 -0.333183197 1.208146841 -0.333183197 1.208146841Orange 0.51833846 0.338526737 1.531159589 0.223236224 -0.559004705 1.595681625 -0.559004705 1.595681625Yellow 0.766976036 0.231182511 3.317621357 0.045134213 0.031250108 1.502701965 0.031250108 1.502701965Green 0.524022574 0.160375458 3.267473597 0.046866283 0.013636291 1.034408858 0.013636291 1.034408858Blue 0.656573637 0.170513769 3.850560819 0.030928603 0.113922724 1.19922455 0.113922724 1.19922455Purple 0.568821485 0.437690549 1.299597367 0.284588639 -0.824105186 1.961748156 -0.824105186 1.961748156Grey 0.524935674 0.2237168 2.346429384 0.100631678 -0.187031031 1.236902379 -0.187031031 1.236902379White 0.718959703 0.233533567 3.078613976 0.054194777 -0.024248334 1.462167741 -0.024248334 1.462167741Black 0.440322817 0.180689378 2.436904822 0.092756107 -0.134711427 1.015357062 -0.134711427 1.015357062Brown 0.435711185 0.221654969 1.965718104 0.144046632 -0.269693852 1.141116222 -0.269693852 1.141116222

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The linear regression for the suicide rates, showed little to no overall statistical significance with

an f-score of 0.640129334. Grey had a high p-value at 0.220482742 and a coefficient of -2.237435209.

None of the colors tested showed a significant p-value. The lowest p-value was purple with a score of

0.19158232. All coefficients were negative values.

Table 2: suicide rate and color analysisSUMMARY OUTPUT

Regression StatisticsMultiple R 0.857703052R Square 0.735654525Adjusted R Square -0.145497056Standard Error 3.759223375Observations 14

ANOVAdf SS MS F Significance F

Regression 10 117.9830315 11.79830315 0.834878516 0.640129334Residual 3 42.39528116 14.13176039Total 13 160.3783126

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 226.1483861 138.5504102 1.63224624 0.201130183 -214.7808549 667.0776272 -214.7808549 667.0776272Red -2.376431446 1.569474275 -1.514157628 0.227212905 -7.371199055 2.618336163 -7.371199055 2.618336163Orange -3.026569884 2.194030274 -1.379456756 0.261596387 -10.00895342 3.955813655 -10.00895342 3.955813655Yellow -2.144547086 1.498320139 -1.431300982 0.247736982 -6.912870477 2.623776305 -6.912870477 2.623776305Green -1.723465884 1.039411576 -1.658116884 0.195874167 -5.031337415 1.584405647 -5.031337415 1.584405647Blue -1.64142971 1.105119122 -1.485296632 0.234142984 -5.158411976 1.875552557 -5.158411976 1.875552557Purple -4.76521059 2.836722212 -1.67982983 0.19158232 -13.79292671 4.262505533 -13.79292671 4.262505533Grey -2.237435209 1.449934019 -1.543128984 0.220482742 -6.851772371 2.376901953 -6.851772371 2.376901953White -2.130026514 1.5135576 -1.407297955 0.254052804 -6.946842304 2.686789276 -6.946842304 2.686789276Black -1.681259191 1.171068404 -1.435662669 0.246607636 -5.408121507 2.045603125 -5.408121507 2.045603125Brown -2.371964673 1.436571055 -1.651129378 0.197278364 -6.943774918 2.199845573 -6.943774918 2.199845573

Conducting a single factor analysis on the colors did reveal some interesting results. The ANOVA

summary showed that grey had the highest average percentage across the 14 cities at 24.80571429

percent. Yellow, which had the highest positive correlation with homicide rates had the lowest at

2.241428571 percent. Black and blue also had high average percentages across cities with an average

percentage of 16.96035714 and 14.31107143 respectively. Red was fifth in the rankings with 10.01.

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Table 3: Anova – Single Factor Analysis on colorAnova: Single Factor

SUMMARYGroups Count Sum Average Variance rank

Red 14 140.14 10.01 35.175677 5Orange 14 42.86 3.0614286 5.1956593 8Yellow 14 31.38 2.2414286 4.7276593 10Green 14 109.34 7.81 52.835738 6Blue 14 200.355 14.311071 89.733701 3Purple 14 34.18 2.4414286 4.3921516 9Grey 14 347.28 24.805714 82.482303 1White 14 103.7 7.4071429 22.474991 7Black 14 237.445 16.960357 92.26894 2Brown 14 163.82 11.701429 59.259413 4

ANOVASource of Variation SS df MS F P-value F critBetween Groups 6524.980104 9 724.99779 16.163279 1.52363E-17 1.952610341Within Groups 5831.101046 130 44.854623

Total 12356.08115 139

Conclusion:

A significant positive correlation between homicides and a preference for the color red was not

supported by the data. Other colors such as blue, green, yellow and white all showed statistically

significant p-values with higher coefficients than red. Yellow had the highest coefficient of any color but

had the lowest average percentage across cities. Thus the changes in values for the color yellow

between cities were on a very small scale. This could be why the p-value for yellow was lower than blue

which had a much higher average percentage of around 24 percent. The larger range in values also gave

blue a lower, more significant p-value. The f-score showed that, the data was significant and further

study would be needed to test whether blue, green, yellow and white can be correlated with aggression

rather than red.

Suicides on the other handed yielded no significant results. The f-score showed no statistical

significance and all p-values were well above .05. An interesting point to make is that grey, black, and

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blue all showed high average percentages across the 14 cities studied. This trend could indicate a

pattern among artists, preferring colors typically associated with depression over bright colors.

Overall, the study showed a significant positive correlation with colors relating to homicide but no

correlation between colors and suicide. Furthermore, the colors that were associated with homicide

were not the colors predicted to be associated with homicide. Grey was predicted to be positively

correlated with suicides but had no statistical significance.

Discussion:

The previous study shows how geospatial sciences can be used in unique lines of investigation.

Mental health has links with geospatial sciences because different locations have a wide range of

cultural, social, and economic differences that can have various psychological impacts. Establishing a link

between artwork, psychological states/traits, and location is not as obvious. The difficulty with this study

is that it is not entirely clear how well color preference is correlated with psychological states in the first

place.

While this study did show some correlations in the data, there are many potentially confounding

factors to take into consideration. It was assumed that homicide and suicide rates in an area would work

as proxies for aggression and depression, but this is a subjective assumption and does not take into

account the myriad circumstances that lead to homicide and suicide. Mental health data on a large scale

can be subjective and difficult to come by and these proxies were used in the absence of quality mental

health data. Unfortunately, homicide and suicide rates provide raw numbers but do not provide for the

most accurate correlation.

Another factor was the selection process for the artwork and the counties to be used for the

study. Often artists are commissioned to create certain types of artwork that may not accurately reflect

the internal state of the artist. A client may want specific colors to be used in the artwork, giving the

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artist less choice in the composition of the art piece. For future studies, it may be wiser to use abstract

artwork rather than mural art. The study also used homicide and suicide rates at the county level,

rather than city level which may have diluted the rates with each county having a wide range of

population densities.

Other potentially confounding factors include false color interpretations by the color

summarizer and small sample size. Some false color interpretations were reinterpreted through human

eyes while other errors such as the elimination of colors from analysis by the summarizer were

unavoidable. The summarizer from TinEye Labs performed well in most cases and it was the only color

summarizer found that could mask unnecessary background colors. The small sample size was mostly

the result of the difficulty in finding verified local artists in each city. The search required large amounts

of time cross referencing artist names with cities on Google. There was also the issue of how an artist

was related to the given location. It was often difficult to determine the length of time an artist had to

live in the location for them to be influenced by that location’s overall mental health trend.

The most interesting aspect of this study was the fact that grey, blue, and black had the highest

average percentages across cities. Artistic expression is often associated with depression and the colors

grey, blue and black are as well. This result could imply that artists are more depressed than the general

population. This could also explain why the results for the suicide rate condition showed no correlation

with these types of colors. If the majority of artists express depressed emotions through their art

already, there would be little difference to discern between cities. The study also revealed that there

could be a potential correlation between aggression and the colors blue, yellow, green and white but

more research would need to be carried out, given some of the limitation of the current study. It would

also be interesting to do more research studying the differences in the color preferences of people who

consider themselves artists and the general population.

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Map 1

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Map 2

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References:

Bell, N. J. (2009). Invited Commentary: The Role of GIS in ForensicMental Health: Challenges and Opportunities, International Journal of Forensic Mental Health. 8:3,169-171, DOI: 10.1080/14999010903358763

Blomdahl, C., Gunnarsson, A. B., Guregard, S., & Bjorklund, A. (2013). A Realist Review of Art Therapy for Clients with Depression. The Arts in Psychotherapy. 40. 322-330. DOI: 10.1016/j.aip.2013.05.009.

Carruthers, H. R., Morris, J., Tarrier, N., and Whorwell, P. J. (2010). The Manchester Color Wheel: development of a novel way of identifying color choice and its validation in healthy, anxious and depressed individuals. BMC Medical Research Methodology. 2010 10:12. DOI:10.1186/1471-2288-10-12

Fetterman, A. K., Liu, T., and Robinson, M. D. (2015). Extending Color Psychology to the Personality Realm: Interpersonal Hostility Varies by Red Preferences and Perceptual Biases. Journal of Personality. 83:1. DOI: 10.1111/jopy.12087.

Holmes, C. B., Fouty, H. E., Wurtz, and Burdick, B. M. (1985). The Relationship Between Color Preference and Psychiatric Disorders. Journal of Clinical Psychology. 41:6. 746-749. DOI: 10.1002/1097-4679(198511)41:6<746::AID-JCLP2270410603>3.0.CO;2-Q.

Hudson, C. G. (2012). Disparities in the Geography of Mental Health: Implications for Social Work. Social Work. 57:2, 107-119. DOI 10.1093/sw/sws001.

Nordbo, E. C. A., Nordh, H., Raanaas, R. K., and Aamodt, G. (2018). GIS-derived Measures of the Built Environment Determinants of Mental Health and Activity Participation in Childhood and Adolescence: A Systematic Review. Landscape and Urban Planning. 177. DOI: 10.1016/j.landurbplan.2018.04.009.

Rentfrow, P. J., Gosling, S. D., Jokela, M., Stillwell, D. J., Kosinski, M., and Potter, J. (2013) Divided We Stand: Three Psychological Regions of the United States and Their Political, Economic, Social, and Health Correlates. Journal of Personality and Social Psychology. 105:6. 996-1012. DOI: 10.1037/a0034434.

Stefano, D. and Fusar-Poli, L. (2018). The Cats of Louis Wain: A Thousand Ways to Draw One’s Mind. Am J Psychiatry, 175:315, DOI: 10.1176/appi.ajp.2017.17111235.

Tao, B., Xu, S., Pan, X., Gao, Q., and Wang, W. (2015). Personality trait correlates of color preference in schizophrenia. Translational Neuroscience. 6, 174-178. DOI: 10.1515/tnsci-2015-0018.

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Websites used for art dataset:

Street Art Cities. Accessed March of 2019 from https://streetartcities.com/#map

Wescover. Accessed March of 2019 from https://www.wescover.com

Dorwart, L. (2018) 10 Artists from Cincinnati, OH, Everyone Needs to Know. Culture Trip. Accessed March of 2019. https://theculturetrip.com/north-america/usa/ohio/articles/10-artists-from-cincinnati-oh-everyone-needs-to-know/

Wood, J. (2014). Emerging Pittsburgh Artists You Should Know. Complex. Accessed March of 2019. https://www.complex.com/style/2014/11/emerging-pittsburgh-artists-you-should-know/

Crush Walls. (2018). Accessed March of 2019. https://www.crushwallsartists2018.com/esic-1/wpkobtobbxr6x1gwnlc183feolygos

Pinterest. (2017). Accessed March of 2019. https://www.pinterest.com/pin/101612535325901870/

Denver.org. Denver Graffiti Tour. Accessed March of 2019. https://www.denver.org/listing/denver-graffiti-tour/24224/

#denverpublicart Medias. Pictame. Accessed March of 2019. https://www.pictame.com/tag/denverpublicart

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