Visualizing
Greed
Using data to visualize greed in Denmark. School assignment @ DMJX May 2022
Section #1
ABOUT
Is God Watching?
One of the deadly sins is greed. But what does greed mean in todays society?
A selfish and excessive desire for more of something (such as money) than is needed motivated by naked ambition and greed. Paul Piff, a social psychologist at UC Berkeley, found that people who “had more” were less ethical, and more likely to lie, cheat, or steal. Interestingly, it wasn’t that more greedy people made it to higher social class, but that getting to a higher social class reinforced those behaviors, making people more likely to be greedy.
Section #2
Choropleth map
Greed of Danish Municipalities
Using data from Statistics Denmark (Danmark Statistik) we came up with an algorithm to calculate a greed index for Danish municipalities. The algorithm uses a weighted average of 4 datasets with a distribution of Danish municipalities from the year 2019.
Least
greedy
Most
greedy
The Datasets
Dataset #1: STRAF11
The first dataset is "STRAF11: Anmeldte forbrydelser efter område og overtrædelsesart" - Reported crimes based on area and kind of crime. We choose 2 views from this dataset. The first being theft and the second being money related crimes. The numbers from theft is the sum of "Andre tyverier", "Tyveri/brugstyveri af køretøj", "Tyveri/brugstyveri af knallert", "Tyveri/brugstyveri af cykel" and "Tyveri/brugstyveri af andet", whereas the numbers from crime related crimes is the sum of "Underslæb", "Hæleri", "Grov skattesvig mv." and "Forbrydelse vedrørende penge og bevismateriale", "Skatte- og afgiftslove mv.". All from 2019 and divided by number of citizens to get theft pr. citizen and money related crimes pr. citizen.
| Municipality | Theft cases | Population | Cases pr. citizen |
| København | 45,745 | 627,321 | 0.073 |
| Frederiksberg | 4,146 | 104,225 | 0.040 |
| Dragør | 228 | 14,365 | 0.016 |
| Tårnby | 2,281 | 42,998 | 0.053 |
| Albertslund | 960 | 27,768 | 0.035 |
| Ballerup | 1,582 | 48,449 | 0.033 |
| Brøndby | 947 | 35,224 | 0.027 |
| Gentofte | 2,666 | 75,112 | 0.035 |
| Gladsaxe | 1,841 | 69,552 | 0.026 |
| Glostrup | 945 | 22,842 | 0.041 |
| Herlev | 1,114 | 28,900 | 0.039 |
| Hvidovre | 1,751 | 53,492 | 0.033 |
| Høje-Taastrup | 1,455 | 50,826 | 0.029 |
| Ishøj | 755 | 22,959 | 0.033 |
| Lyngby-Taarbæk | 2,336 | 56,105 | 0.042 |
| Rødovre | 1461 | 40,357 | 0.036 |
| Vallensbæk | 356 | 16,655 | 0.021 |
| Allerød | 458 | 25,686 | 0.018 |
| ... | ... | ... | ... |
Dataset #2: BIL54
The second dataset is "BIL54: Bestand af motorkøretøjer efter område, køretøjstype, brugerforhold og drivmiddel" - Collection of motorized vehicles based on area, type of vehicle, type of use and fuel source. For this dataset we choose personal vehicles for private use. Then, we took the average value of the months from year 2019 and divided by the number of citizens to get personal vehicles pr. citizen.
| Municipality | # of Cars | Population | Cars pr. Citizen |
| København | 122,438 | 627,321 | 0.195 |
| Frederiksberg | 25,33 | 104,225 | 0.243 |
| Dragør | 6,52 | 14,365 | 0.454 |
| Tårnby | 15,635 | 42,998 | 0.364 |
| Albertslund | 9,388 | 27,768 | 0.338 |
| Ballerup | 18,538 | 48,449 | 0.383 |
| Brøndby | 12,763 | 35,224 | 0.362 |
| Gentofte | 29,285 | 75,112 | 0.390 |
| Gladsaxe | 23,904 | 69,552 | 0.344 |
| Glostrup | 8,705 | 22,842 | 0.381 |
| Herlev | 10,757 | 28,900 | 0.372 |
| Hvidovre | 18,137 | 53,492 | 0.339 |
| Høje-Taastrup | 20,01 | 50,826 | 0.394 |
| Ishøj | 8,154 | 22,959 | 0.355 |
| Lyngby-Taarbæk | 20,931 | 56,105 | 0.373 |
| Rødovre | 14,053 | 40,357 | 0.348 |
| Vallensbæk | 6,477 | 16,655 | 0.389 |
| Allerød | 11,882 | 25,686 | 0.463 |
| ... | ... | ... | ... |
Dataset #3: FORMUE2
The third dataset is "FORMUE2: Familiefordelt nettoformue efter område og komponenttype" - Fortune distributed by family based on area and type of asset. For this dataset we used the raw numbers from year 2019.
| Municipality | Family Fortune |
| København | 1,534,339 |
| Frederiksberg | 2,727,068 |
| Dragør | 4,165,247 |
| Tårnby | 2,406,733 |
| Albertslund | 1,616,375 |
| Ballerup | 2,162,556 |
| Brøndby | 1,641,361 |
| Gentofte | 6,600,417 |
| Gladsaxe | 2,366,187 |
| Glostrup | 1,886,689 |
| Herlev | 2,044,867 |
| Hvidovre | 2,017,982 |
| Høje-Taastrup | 2,005,200 |
| Ishøj | 1,512,444 |
| Lyngby-Taarbæk | 4,253,608 |
| Rødovre | 1,959,823 |
| Vallensbæk | 2,775,709 |
| Allerød | 4,037,498 |
| ... | ... |
Dataset #4: FOLK1A
The fourth data set we used was "FOLK1A: Folketal den 1. i kvartalet efter område, køn, alder og civilstand" - Number of citizens 1st day pr. quarter based on area, sex, age and civil status. We used this dataset to calculate the pr. citizen values.
| Municipality | Population |
| København | 627,321 |
| Frederiksberg | 104,225 |
| Dragør | 14,365 |
| Tårnby | 42,998 |
| Albertslund | 27,768 |
| Ballerup | 48,449 |
| Brøndby | 35,224 |
| Gentofte | 75,112 |
| Gladsaxe | 69,552 |
| Glostrup | 22,842 |
| Herlev | 28,900 |
| Hvidovre | 53,492 |
| Høje-Taastrup | 50,826 |
| Ishøj | 22,959 |
| Lyngby-Taarbæk | 56,105 |
| Rødovre | 40,357 |
| Vallensbæk | 16,655 |
| Allerød | 25,686 |
| ... | ... |
Combining Datasets
To combine the 4 different views we got, we mapped them to a common range of 0-100. We then weighted them like this: Theft = 2, Money related crimes = 2, Collection of vehicles = 1, Family fortune = 1. This collective value is then remapped into the common range of 0-100 once again. This is our algorithm.
Section #3
GRAPHS
Have the Danes Become More Greedy?
Using data from Statistics Denmark (Danmark Statistik) we decided to examine if the danes have become more greedy over the past years? We used the datasets:
Money and Tax Related Crimes
Collection of Vehicles
Family Fortune
Section #4
Choropleth map
Opt Out of Danish Church
This map shows how religious the Danish citizens are, or the lack of it. The yellow spots indicate people unsubscribing to the Danish church. Is there a correlation between committing a sin of greed and how detached one is, to the church? Not strong, although interesting.
Fewest
exits
Most
exits
Section #5
PEOPLE
The Greedy Team
Caroline
Age
24
Municipality
København
Act of greed
One night some years ago I stole a French bulldog. I brought him home because I wanted him. Luckily it wasn’t a real dog. It was a porcelain dog.
Cornelius
Age
26
Municipality
Odsherred
Act of greed
My name is Cornelius, and when I was 15 years old I committed a crime in the name of greed. I stole approximately 150kr worth of liquorice. I walked around Aldi with my friends, and when we got to the counter, the cashier already knew - I got busted. I was not good apparently.
Eigil
Age
24
Municipality
Ishøj
Act of greed
I borrowed my parents rejsekort without paying.
Signe
Age
24
Municipality
København
Act of greed
When I was about 10 years old I committed a crime.
On a trip to a “Legeland” with the after-school-club for primary school kids I played the arcade machine where you can win Toys and teddybears. After losing all my
pocket money to the machine I got greedy and stuck my arm in the machine to get a toy out. I ended up stealing a teddy bear. Eventually a pedagogue busted me.