With those thoughts in mind, I present the methodology, the decisions, and the visualization of over 200 deaths at the KZ Porta Westfalica Barkhausen, during a one year period.<\/p>\n
A change is happening (at least for me) in how data is analyzed. I have a spreadsheet of over 200 deaths, with various information, death date, location, nationality, etc. The desire to create a visualization came from wanting to understand the data and see the commonalities and differences. The first question I had was how many nationalities are represented, and which countries. The second question is what is the distribution of the deaths by month.<\/p>\n
The following is how I came to a visualization that answers the first question.<\/p>\n
Data Compilation<\/h2>\n
Data is taken from two locations and merged.<\/p>\n
- \n
- The first set of data is a large spreadsheet obtained from the KZ Neuengamme Archiv containing all of their data on the prisoners that died and were at KZ Neuengamme or one of the satellite camps. This file contains 23,393 individuals.<\/li>\n
- The second data set is another set of files from KZ Neuengamme Archiv, but is derived from a list compiled by French authorities. It is available online at: http:\/\/www.bddm.org\/liv\/index_liv.php. The files were split into three sections listing the dead from Barkhause, Porta Westfalica, and Lerbeck. These files contained a total of 177 individuals.<\/li>\n<\/ul>\n
Combining just the individuals matching those who were in a Porta Westfalica KZ from both sets of data left around 280 individuals.<\/p>\n
Data Cleaning<\/h2>\n
There were a number of steps needed in order to have useful information from the data.<\/p>\n
- \n
- First of all, the data from the French archive was highly abbreviated. For example, the column containing the locations of internment were two or three letter abbreviations of location names. Elie Barioz, for example, had the locations “Wil, Ng (Po, Bar)” which, when translated, turn into “Wilhelmshaven, Neuengamme (Porta Westfalica, Porta Westfalica-Barkhausen)”\n
- \n
- The process of translating the abbreviations was quite labor intensive. First, I had to search on the French site for an individual:\u00a0<\/span>http:\/\/www.bddm.org\/liv\/recherche.php<\/a><\/li>\n
- Search for ‘Barioz’.\u00a0
![\"image-of-searching\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-of-searching-300x169.png\")
<\/a>Note: The Chrome web browser can automatically translate the pages on this site.<\/li>\n- The correct individual can be determined by comparing the full name and the birthdate. The citation to the location in the book is a hyperlink to that record (ex. Part III, No. 14 list. (III.14.)).
![\"image-of-matches\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-of-matches-300x188.png\")
<\/a><\/li>\n- The abbreviations for this individual’s interment locations are hyperlinks to more information, part of which is the full name of the location. Clicking on ‘Wil’ results in a pop up window describing the KZ at Wilhelmshaven and information about the city.
\n![\"image-location-pop-up\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-location-pop-up-300x143.png\")
<\/a><\/li>\n- After determining that ‘Wil’ meant ‘Wilhelmshaven’, all occurrences of ‘Wil’ in that column can be changed to ‘Wilhelmshaven’.This process is repeated until all of the abbreviations have been translated.<\/li>\n<\/ul>\n<\/li>\n
- Remove extraneous asterisks. It was quite frustrating to note that the French site did not include information on what the asterisk and other odd symbols mean. (Another odd notation is the numbers in parenthesis after the birth location.) I had to simply just delete the asterisks, losing any possible meaning they might have had.<\/li>\n
- Combine duplicates. Keep as much information from both records as possible.<\/li>\n
- Fix dates. They should all be the same format. This is tricky, in that Europe keeps dates in the format MM-DD-YYYY. For clarity sake, it would be best to use “Month DD, YYYY”. I left them as is for now. Editing 280 dates is not fun…<\/li>\n
- Fix nationality. The Tableau software references current nations. The data in the spread sheets uses nations current to the time of creation. For example, some individuals were noted with the nationality of ‘Soviet Union (Ukraine)’. These needed to be brought to the present as ‘Ukraine’. More problematic were the individuals from ‘Czechoslovakia’. Presently, there is the Czech Republic and Slovakia. The question is, which present day nationality to pick. There is a column for birth place which potentially solves the issue, but this field is for where the individual was born, wich, in the case of Jan Siminski, is seen. He was born in the Polish town of Obersitz (German translation), so the birth place can not clarify his nationality as Czech or Slovakian.<\/li>\n
- This brings up another issue, the translation of place names. City names in German, especially during the Third Reich, are different than current German names for the city, which are different than the English name of the city, which are different than what the nation calls the city. I need to standardize the names, picking, probably English. Tableau seemed to have no problem with the ethnic city names, or the German version, so I left them as is.<\/li>\n<\/ul>\n
<\/p>\n
Tool Picking<\/h2>\n
I used the free program, Tableau Public:\u00a0http:\/\/www.tableau.com\/<\/a><\/p>\n
This allows for very quick visuals, and a very easy process. The website has a number of free tutorials to get started. http:\/\/www.tableau.com\/learn\/training<\/a><\/p>\n
Map<\/h2>\n
The first visualization I wanted to make was a map showing where the prisoners were from, their nationality. The map would also show the number of prisoners from each country. (This is not a tutorial on how to use Tableau, but a walk through of the pertinent choices I made to make sense of the data, it is methodology, not tech support. \ud83d\ude42 )<\/p>\n
Using the default settings (basically, just double clicking on the Nationality field to create the map) results in a dot on each country represented in the data.<\/p>\n
![\"image-blue-dots-map\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-blue-dots-map-1024x549.png\")
<\/a>
\nThis can be transformed into a polygon highlight of the country by selecting a “Filled Map”.<\/p>\n![\"image-filled-map\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-filled-map1-1024x536.png\")
<\/a>
\nNext step was to apply shading to the filled map; the larger the number of prisoners who died from that country the darker the fill color.
\n![\"image-filled-map\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-filled-map-1024x548.png\")
<\/a>
\nThe default color was shades of green. I wanted a more dull color to fit in with the theme of the visualization, “death”. I picked a light orange to brown default gradient, separated into 13 steps (there are 13 countries represented).<\/p>\nTable<\/h2>\n
While just a filled map with gradient colored countries is helpful, the information would be more complete, more fully understandable, with a legend. This can be created by using a plane table listing the countries and the number of dead from that country. Each row is color coordinated with the map by using the same color scheme and number of steps as with the map.<\/p>\n
![\"image-table\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-table-1024x661.png\")
<\/a><\/p>\n<\/p>\n
Dashboard<\/h2>\n
In Tableau, you create a dashboard to combine the different work sheets, maps, tables, graphs, etc. In this case, a full page map, with the table overlaid completes the visualization.<\/p>\n
Result<\/h2>\n
The result is a very simple map, created in about ten minutes (after a few video tutorials to refresh my memory on how to create the affects I wanted).<\/p>\n
(See a fully functioning result below this image.)
\n![\"image-final-result\"](\"http:\/\/nazitunnels.org\/wp-content\/uploads\/2015\/10\/image-final-result-1024x537.png\")
<\/a><\/p>\n<\/p>\n
Benefits of Tableau<\/h2>\n
Tableau has some limitations. The results are hosted on their servers, which has the potential for lock down. They use proprietary, closed source code and applications.<\/p>\n
- Search for ‘Barioz’.\u00a0
- The process of translating the abbreviations was quite labor intensive. First, I had to search on the French site for an individual:\u00a0<\/span>http:\/\/www.bddm.org\/liv\/recherche.php<\/a><\/li>\n
- First of all, the data from the French archive was highly abbreviated. For example, the column containing the locations of internment were two or three letter abbreviations of location names. Elie Barioz, for example, had the locations “Wil, Ng (Po, Bar)” which, when translated, turn into “Wilhelmshaven, Neuengamme (Porta Westfalica, Porta Westfalica-Barkhausen)”\n