Blog For Big Data

Real-life examples of environmental analytics A real life example of citizen science analysing the environment is that it can contribute to better and more accurate evaluations of human infrastructures in wildlife such as recording roadkill accidents so that it can be made available to emphasise how much roads can impact wildlife.

Citizen science can be applied to any and all kinds of scientific observations which require scientific monitoring data. Weather phenomena, climate change impacts in the environment, monitoring of local air quality and also monitoring the effects and impact of climate change on different species of animal. Citizen science for climate simulation and ecology can be incredibly helpful in monitoring many things such as monitoring microplastics in water so that people can be more aware of what products contribute to this and lead efforts to reduce or change the process in which manufacturers produce these products to eliminate it from water sources. 

As with all things there will always come limitations to data analysis due to the fact that it is created by humans and is subsequently subject to human error. Some of the limitations that you may come across would be that the data may be incomplete, whether it be missing values, or lack of a section of necessary data. This could severely limit the data's usability. Survey data can also be scrutinised due to the human component. People do not always provide accurate information through surveys and many are likely to not answer truthfully. For example if a person were asked how much alcohol they consume within a week they are likely to say less than their actual intake. 

Two of the biggest challenges of big data is Analysing and Visualising the data. Firstly with analysing the data, the size of big data files can sometimes be substantial, there are many things that must be considered before downloading the data, for example the file size, how long the data file will take to download, will all of it be necessary or will part of the file suffice and is there enough storage space within the system itself. Visualisation is way to represent the data in a way that is easier to understand such as word clouds and things of the like. This will aid users in seeing the prominent and key terms from the analysis of the data sets. The first step after downloading the data would be to quality check it to ensure that each field had the appropriate data types in each field and to ensure that the user understood the meaning of each field. Keeping a copy of the original data would be essential as well as each documented version change for each stage of visualisation.

With big data becoming much more important in recent years the need for data scientists has also increased. These data scientists are educated in discovering, collecting, processing, analysing and presenting information gained from these large amounts of data and often compare and combine it with different sources. The skills these data scientists must have are data visualisation skills, statistical skills, data processing and systems engineering skills, they must have an understanding or have a sufficient level of skill with programming languages as well as mathematical and analytical skills.

Open data has been increasing for some time now with data being made open on various sites globally. There are many advantages to having open data, these advantages include being able to share public data sets so that they can be compared. These open data sources can also be used for environmental purposes or even health issues. Disadvantages of open data would include the fact that the site providing the data would be inherently biased and formed in the opinion of the creator.