in the infographic an average individual is the unit. There are obviously several assumptions about this individual: it is probably living in a house (needs to water a garden) and owns a car. The average use seems to be based on classic statistical calculations and probably starts with household based and billed water use. all kinds of questions then arise on how to make this more complex or at least draws attention to the question of how decisions were made of the different values and calculations on the way. It is also interesting to think of alternative units of analysis/observation: water scarcity is usually a communal / regional issue and situating water usage in more specific contexts could be another logic. Looking at water conflicts and the governance of water as a scarce ressource could help to see the different logics and debates that feed into claculating water.
the unit of observation is the count of person detected by the devices sensors during one hour
Each row considers the number of pedestrians at a certain location for a specific hour and weekday and the temperature and weather conditions. Eventhough the direction of the movement of the pedestrian is supposedly accounted for by the scanner, I have not found any information about this (neither in the data documentation nor in the dataset). Again, this seems to indicate different purposes: if one is interested in how many people pass by a shop, directions is not that relevant. If one wants to know how many people are attending a specific event, it might be relevant. Interestinly, it provides data that is of no interest to the users (interview: we are not interested in data beyond the open shop hours). The interview also indicates that more context-data is added (times of sale, Black Friday, or a specific event) to make sense of the data. How is it decided which cata need to be counted automatically and which are added "as common sense". the data obviously can only tell that and how amny pedestrians moved through the scanner, but not why and for what purpose.
The dataset shows how households, including per definition given in the datasheet also small business enterprises (with less than 60.000 € profit per year according to the legal definition of the german term), use fresh water. It therefore assembles average data from German households over the course of the year 2021 and breaks it down to Liter per person per day.
Each row represents one usage-category for fresh water such as the usages "Bathing, showering", "toilett", "cleaning, garden and car cleaning" etc.. The rows already bundle diverse practices into these categories. In total, 7 categories are given.
The second collumn provides the percentage of the total water spendings that is attributable to the category, the third collumn presents the number of liters used per person per day for the mentionned activity.
From the footnote I draw, that the numbers in the third collumn are not part of the inquiry of BDEW, the data source, but product of a simple calculation of the percentage in collumn 2 with the liter/person and day-figure that the Statistisches Bundesamt gives for the water consuption (127 liter/person and day)
In this dataset each row is paragraph of text. The text is a market sentiment that might have been published in the news media or the anual busniess report. Each of these texts is qualified as either "positive", "negative" or "neutral".
a gruop of people (gathered by choice or location) who are taking part in the profitable exchange or receiving of money for weapons and the area affected by that exchange
An arms deal specifying which country is selling a specific item to another specified country, which company is involved and what conflict is concerned.
