Geolocation applications and those that, without being specific, include the spatial dimension, are becoming more numerous. Not in vain, even though they are heterogeneous, many of the data generated by Big Data and the Internet of Things –IoT– are characterized by the fact that they have a geographical component.
In the same way that happens with raw materials, large sets of data offer endless possibilities but without being processed they do not contribute much. In other words, without the spatial analysis of the data, it is difficult to transform the data into useful information.
In this article we will focus on this interesting scenario, especially on the effort that must be made by the applications based on geolocation to offer a wide range of services.
Objective: Extracting Value to Big Data
The great advantages of meeting these challenges imply no less important challenges. At the technological level, the large data demand to resort to solutions that can extract their value effectively and at low cost.
It is this efficiency that allow us to use frameworks and its rich open source ecosystems which allows us, in the last instance, to make the great leap so that the geolocation apps can reach the next level thanks to Big Data.
For design the applications, it is required to develop a strategy considering both usability and the importance of preserving the privacy, ensuring that they are guaranteeing security. As an added value, apps that include location-based features dove prominently with mobile solutions and undoubtedly significantly improve the user experience.
There are some requirements that are mandatory for any mobile solution or strategy that requires apps capable of offering geolocation services, either as a main or as a complement. One of them is that it is required to store and analyze ad hoc data to satisfy the user, offer speed and support searches or queries.
In addition, it is essential to have an infrastructure that supports these requirements, in order to take geolocation apps to the next level that puts us on the shelf of the current digital context. Only in this way will it be possible to take advantage of spatial data sources for conversion into geographic intelligence .
Geolocation Apps: Becoming a bigger player
The current digital era, provides countless possibilities in order to take advantage of the data value -including geospatial information- it can be translated into advantages that have an immediate impact on key areas such as more informed decision, greater functionality and personalization.
The areas that can benefit from geolocation apps of the most different nature are equally varied due to the endless possibilities that the exponential increase in Big Data and the Internet of Things – IoT – are opening in terms of new technologies and solutions, fully equipped with the required efficiency.
The amount of softwares that can take advantage of the key functionalities provided by the geolocation are innumerable. Not only in terms of the areas involved, but also in terms of the potential of Big Data as an engine of innovation.
It is proved by the fact of attending to a revolution of the data generated by the same activity of the network and millions of connected devices, as well as the very existence of a technology capable of taking advantage of them.
In this unprecedented context in the history of humanity, what we know as “geolocation” already in itself constitutes a sum of technologies GIS and Big Data. Although providing services of this type is not a novelty, certainly, yes it is to do it in this context.
The immense possibilities that open up to apply the geospatial techniques to deal with the great data can be very different, but above all, they have in common the necessity of optimization.
It is not a question of following a single technological model, in contrary, apart from the fact that good practices can be extrapolated, in the design of geolocation applications, it is mainly sought that the knowledge of the geospatial or geosocial environment is practical.
It will be this objective that lead actions that needs to combine different technological models that help us to offer a better service, such as augmented reality. And of course, the same structured and unstructured data we have within our reach represents a potentially useful reef.
The areas of implementation are so many, especially at a hypothetical level, that it would be futile to try to make a closed list. In general, the leading areas that best results are harvested range from industry to manufacturing, logistics and decision making at BI level to, for example, health, transportation or smart cities.
In health care, important advances have been made in patient monitoring, adding efficiency in the management of resources and a more efficient care for the user and progress through data analysis.
Substantial improvements have also been made in the development of apps for the proactive maintenance of different technologies that, in one way or another, depend on geographic location.
Summarizing, it is important to highlight, the wide range of existing and emerging applications that can take advantage of Big Data for processing through principles and techniques related to geolocation is innumerable.
Among other objectives, being able to capture, store and process large data in real time and combine them with historical data gives us one of the essential keys to successful apps. The fact that they are different and require adapted architectures does not prevent them from meeting a series of common requirements that are largely determined by the Big Data universe. Its development does not have to find dissimilarities as an obstacle. Moreover, in the common aspects of that difference maybe you will find the inspiration of each line of code. This is one of the greatest challenges.
