How much do London firefighters spend on saving helpless kittens? The Open Data answers

In this second post, a continuation of “Smart Cities: Squeezing Open Data with Power BI”, we will analyse the problem and understand the measures taken by London Firefighters in view of these results as they try to alleviate the situation.

Hypothesis

The time has come to consider what information we want to extract from the data, what answers we are looking for. Some questions may be clear from the beginning of the analysis. Others, however, will emerge as the data reveals more information.

The problem: The alarming signal that led us to consider the analysis is the increase in the number of interventions by the fire department to perform these services, and the associated cost.

Figure 1: Image of the campaign launched in 2016.

We begin to hypothesize what we will try to show with the data. With the conclusions we obtain, we will look for strategies or define initiatives that allow us to solve the problem or reduce / minimize its effects.

• Hypothesis 1: The number of services increases each year. If no corrective measure is considered, the cost will continue to increase.

• Hypothesis 2: The type of animal involved in the incident is essential when discriminating whether or not the intervention of the fire department is really necessary. The location of the incident (rural or urban) may also be related to the type of animal.

The first step is to take a look at the data. Load them as a table, create the names of the fields (some of them will be descriptive, others not), and try some filters. This brief preliminary exploration will help us choose which fields can provide the most relevant information for each report.

To work on Hypothesis 1, we will choose the “Line Chart” display. We select the sum field ´CalYear´ (calendar year) and drag it under the “Axis” label to represent this value on the vertical axis (ordered) and the PumpCount field we drag under the label ” Values “to appear on the horizontal axis (abscissa). If we directly select the fields on the list, we can add them in an order that is not the one that interests us, that is why it is better to drag them directly to their final position.

Figure 2: The field CalYear (year) must appear under the label “Axis”, while the field “PumpCount” (number of cases) must appear under the label “Values”.

Figure 3: Evolution of the number of cases per year.

Figure 4: Example of the advanced filter. It only shows the years before 2016

Figure 5: Evolution of the number of cases per year (filtered).

Figure 6: Display example “Funnel”.

Figure 7: Evolution of the service cost per year.

Figure 8: Number of services by type of animal.

Figure 9: Cost of the service by type of animal in 2016.

Figure 10: Geographic distribution of notices regarding the rescue of cats (in yellow) and dogs (in orange).

Figure 11: Geographical distribution of warnings regarding the rescue of large animals such as bulls (in red), cows (in gray) and deer (in blue).

Figure 12: Example of clearly erroneous data due to duplications of names, numerical codes etc.

Figure 13: Appearance of the visualisations with two different visualizations (table and funnel) after applying the segmentationBorugh = “City of London”

Figure 14: London Firefighters Campaign 2012: “I am an animal, get me out of here”.

This has been a very simple example, but with palpable results. If in 2016 we discounted the outputs of the Fire Department relating to mishaps of domestic or small animals, which we considered “avoidable”, the savings would have been £ 215,160.

If we take into account the potential of applying Data Science to the entire arsenal of data collected and stored by institutions and companies today, we realize the great opportunity we have to improve our environment and our lives. Let’s take it!