Big data is a collection of enormous amount of information which can be enriched by contextualizing it. Of all the additional information, location is the most readily available data. What does this information offer and how best to exploit it by the process of mapping?
When surfing on computer or smartphone, a user can leave a lot of personal information (digital footprint) such as age, gender, profession and most importantly, his current location. Information about user’s location can be transmitted even without his or her knowledge. This can happen via ‘in-built’ mobile applications on phone or sometimes the smartphone requires you to enable location sharing in settings for it to function properly. In its study ‘Mobilitics’ in 2016, CNIL revealed that on the 6 smartphones tested for 3 months, 30% of the applications used had accessed geolocation information and that Facebook app recorded the device’s location once per min and Play Store 10 times per minute. Location is therefore the most important data and it’s likely to gain greater significance in future with the proliferation of IoT related services. This data revolution has led to the emergence of new service models which are based entirely on knowledge of geodata. For instance, logistics & delivery, transport (VTC) and dating services (apps that locate a suitable date).
The most ‘natural’ way of communicating any location is by making its map i.e. ‘mapping’. This tool is a means of visualizing and therefore of apprehending a volume of information which is otherwise incomprehensible to human mind. It not only gives ample information but also helps in making predictions.
By combining mapping and data processing, data management actors can develop tools that aid decision-making. For instance, Netflix has internalized the approach of its data visualization tool. All employees in this company manage their activity with the help of information derived from data visualization. Technical services such as monitoring network quality and accidents are also carried out with the help of data and program designs. Even to select the main actor and director for the series ‘House of Cards’, the company referred to the data showing preferences of the viewers of the original British series. This has turned out to be a profitable strategy for this true player and on the same lines, it renews 80% of its original series for a second season.
Mapping can also derive information from small data. Thus, a specialist in predictive analytics shares in an article the 3 lessons learned while mapping the data on droughts in United States. The first lesson is that by using small data, which is in this case the rainfall survey data over a week (instead of considering the data of a whole year), the map displayed immediately comprehensible information, a readable ‘picture’. The second map he discussed displayed drought data from the same week in 2015 and 2016. The areas which were affected by drought during these two successive years obviously overlapped but it also clearly showed the other areas being affected by climatic changes. Just consulting the statistical data would not make it possible to understand the movements of droughts from one year to another. The third map revealed that drought also affects cities in areas which are usually spared. This information highlights a more important issue: the climatic conditions and the patterns of consumption of natural resources are changing throughout the country and this calls for action to preserve nature.
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