Males are more likely to be accused of homicide or victims of homicide than their female counterparts. In the two charts below, I have focused the data visualization firstly on both male and females accused of homicide in 2011. The numbers are shocking: 507 men were accused of homicide in 2011; compared to only 58 women.
When looking specifically at age, men tend to commit homicide between the ages of 18-24, while women aged 25-29 have a higher rate of committing homicide.
We see countless stories in the media of armed and dangerous men that we must locate because, low and behold, they are wanted for some form of violent crime. Women don't saturate the media in the same way, and when looking at the numbers, it's clear to see why.
It's no surprise why males are accused more of homicide when we have such prominent cases such as the Surrey Six shooting back in 2007 - all of the accused were men and every single victim were also men. When it comes to gang life, males tend to be the ringleaders in both murder and victimization.
When looking at the charts below, the age group to note for males is the 18-24, which are the prime years that males tend to show their aggression, resulting in higher rates of homicide. For females, the age group jumps to the 25-29 year category, with 18-24 not far behind.
Females don't usually become gang members, but they can be quite dangerous, too. Especially when looking at news stories of women accused of murder, their seems to be a trend for domestic violence. Like North Delta woman, Beatrice Thomas, for example. She was accused of stabbing her common-law husband to death during a fight back in 2011 - popping her on the chart below in the 30-39 category! It's a bit odd, but while researching stories of homicide among women, I was unable to find any new stories about women accused of homicide in 2011 between the ages of 18-24, and 25-29.
Women, in general, aren't common in murder stories. Which made me wonder is this to do with the actual numbers behind it, or does the media choose not to report on murderous women? Nevertheless, I found the numbers interesting, but not surprising when comparing males and females.
However, what I did find surprising was the number of male victims in comparison to female victims from 2011.
Though the numbers are not as dramatic as the accused of homicide numbers, I expected that females would be victimized more than males. Yet, looking at both charts below, you can see that males are victims twice as much as females are. I was curious as to why, and specifically looking at the age groups - the most victimized males fall between the 40-49 year old group. Which I personally found very surprising.
After reading an article posted on Canadian Press, 2014 stats show that aboriginals accounted for almost one quarter of all victims, with "aboriginal males...seven times more likely to be homicide victims compared with aboriginal males." Given that aboriginals sometimes live in confined areas, tensions could be high, resulting in more disputes and heightened aggression.
When looking at the chart below, female victims aged 18-24 take the cake, while 60 years and over comes in second. Given that seniors are more vulnerable members in our society, it makes them an easier target.
Well, except for the 'Granny Ripper'...she's more badass than most senior citizens.
In conclusion, I was surprised by the number of male victims, yet enlightened by the reasons as to why males are more likely to be accused of homicide as well as victims. Gang life, aggression, domestic abuse and disputes between aboriginals are some compelling reasons for the gap between males and females.
Crystal Scuor's Data Blog
Monday, December 7, 2015
Monday, November 9, 2015
First Tableau Chart
This is a tableau Public Bar Chart...
That was a tableau public bar chart.
That was a tableau public bar chart.
Friday, November 6, 2015
Assignment 3 - Update 2
Update 2
Disclosure: As you can see, I have changed my data set to that of Victims and Persons Accused of Homicide. The Food Vendor data set proved to be of no use to my final report.
As of right now, I have chosen the above story to accompany my data as it shows that men (of aboriginal decent) are more likely to be murdered than woman (of aboriginal decent). This highlights the fact that males are most likely to be victims, but specifies a minority group rather than males in general. I’m hoping to find a better story that will focus on males as both victims and accused.
1. Lead: Homicide in Canada: Males aged 18-24 are more
likely to be victims of homicide and culprits of attempted homicide.
This is a link to my data showing the Top 20 results of both victims and people accused of homicide in Canada, filtered by their age group. The groupings are as follows: 0-11, 12-17, 18-24, 25-29, 30-39, 40-49, 50-59, and 60 and over. The results of the slice of data shown are the twenty highest numbers, ranged between all age groups. The age groups that are not shown are in the full set of data. I chose to show the Top 20 to highlight the evidence that males in the category of homicide victims and persons accused of homicide are the highest in the age group of 18-24. In a legend to the right of the data, I have calculated the total population in 2011 for each age group by gender, to show an actual percentage rate of crime when I do my final report.
As of right now, I have chosen the above story to accompany my data as it shows that men (of aboriginal decent) are more likely to be murdered than woman (of aboriginal decent). This highlights the fact that males are most likely to be victims, but specifies a minority group rather than males in general. I’m hoping to find a better story that will focus on males as both victims and accused.
Friday, October 16, 2015
Assignment 2: Data Update 1
For my final report, I will be using the “Food Vendors”
dataset. This dataset includes the location and information about the food
vendors specifically on the streets of Vancouver. It is not inclusive of
roaming food vendors. The URL for my dataset is: http://data.vancouver.ca/datacatalogue/foodVendors.htm
This dataset contains the key, latitude, longitude, vendor
type, status, business name and the location of the food vendors on Vancouver
streets. Here is a list of the attributes in detail:
·
KEY: A
unique identifier for each street food vendor business
·
LAT: The
latitude of the vendor measured from the equator in degrees.
·
LON: The
longitude of the vendor measured in degrees from the Zero Meridian.
·
VENDOR_TYPE:
The type of vendor business.
·
STATUS:
Either as open (food cart permit has been issued) or Pending (permit is in
application, evaluation or renewal stage)
·
BUSINESS_NAME:
The business name of the vendor.
·
LOCATION:
The approximate location of the vendor business.
·
DESCRIPTION:
The type of food the vendor sells.
I don’t understand why some business names are left blank in
the Excel file. The dataset attributes explains that if this field is left
blank, then it is similar to the Description field data. Even though it
describes the type of food, it still leaves me wondering what the actual
business name is. I could find out the real name by wandering the streets of
Vancouver and locating each business, although this would take too much time.
Some questions I hope to answer with my data include:
1. What are the most common types of street vendors in Vancouver?
2. Where is the most populated area with food vendors in Vancouver?
3. How many street vendors are open or pending?
1. What are the most common types of street vendors in Vancouver?
2. Where is the most populated area with food vendors in Vancouver?
3. How many street vendors are open or pending?
Monday, October 5, 2015
Assignment 1 - Data Viz Analysis: Italy Burns
While searching tableau
public, I came across an interesting data visualization of the amount of forest
fires per year in Italy. Being Italian myself, I found this visualization to
catch my attention immediately because of the headline, “Italy Burns: The Business
of Summer Wild Fires.”
Link:
Firstly, the title itself
contradicts the actual data visualization (highlighted in red box – although the
title is in Italian, I translated it into English), given that the chart only
shows the amount of forest fires per year. Yet, in surrounding text around the
chart, the creator claims “the main cause of 7,700 detected [forest fires] per
year in Italy in the last 20 years – which burnt a surface as big as Latium –
is arson for profit reason.” Where exactly did the creator get this
information? There are no links or sources to back her fact up, yet she makes an
incredible assumption that these fires are happening for the sole reason of
profit.
How does this affect the
data viz aspect? Considering the claim and the title, the data viz does not
even match up. In her chart, the creator shows only the amount of fires per
year. Nowhere on the chart are there any profit numbers to back up her title.
Moreover, the chart itself is flawed, too.
Like discussed in class, it
is crucial to look at a bare chart – a chart that without numbers to accompany
it, will still make sense. If you take away the numbers above each little fire
bar, the chart is very unclear as too exactly how many fires are happening each
year (highlighted in green box). Yes, we can see that there are fluctuations in
numbers over the years, but we are left to guess what exactly the creator is
trying to compare. A more appropriate chart to show data over time would have
been a line chart, which I feel the creator should have chosen instead.
I commend the creator’s
efforts in attempting to use a fancy fire bar chart in order to show the amount
of fires per year. She did start with a baseline of zero, which ensures that
the data is correct and does not mislead us with false information.
Overall, I feel the creator
should have chosen a different chart (line chart) and should have included
figures of dollar amounts that she feels are attributed to arson profit gain. If
the story truly is about the “business of summer wildfires,” then the creator
should have found a way to show the audience rather than tell us without some
sort of source or number value to accompany the data visualization.
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