Geo-spatial data is data related to geography. It has a location on the surface of the earth. There are two types of spatial data; vector and raster. Vector uses points, lines, and polygons to represent streams, roads, and cities. Raster data uses cells to represent them. Cities are a single cell, roads a sequence of cells and streams adjacent cells.
Geo-spatial data analysis involves collecting, manipulating and analyzing data that has some geographical information. The data is collected using satellites, GPS and drones. It is later analyzed using GIS which means Geographic Information System. It is a technology-based system that integrates geo-spatial data and tabular data to map, analyze and access real-world problems.
Tabular data is additional information about geo-spatial data. It is also known as attribute data. For instance, if company X is based in New York. New York is the geo-spatial information. The name of the company, the products it offers and the number of customers are attribute data.
GIS uses analytical tools and statistical methods to analyze attribute and geographical information. It is an essential problem-solving tool. It aids in identifying areas affected by natural disasters and predicting areas prone to the crisis. For instance, if there is a disease outbreak in a region. Geo-spatial data analysis helps in identifying the regions where the disease is likely to spread.
GIS also assists companies in choosing a strategic point. It identifies locations saturated with companies supplying the same products. This helps start-ups in choosing areas that are less flooded. In case the company is dealing with long-distance trucks, geo- spatial data analysis is essential for identifying the best routes.
Geo-spatial data can detect areas that will experience disasters and climate change. This aids in early preparation and putting necessary measures to mitigate it. In addition, the analysis helps in discovering wet-lands, where agricultural practices can thrive. The public and the government should prevent pollution in such areas.
There are four components in GIS that are essential in making spatial data analysis effective. These include people, hardware, software, and data. People represent the staff members who handle the application, they should not only be effective but also competent and able to deliver quality results.
Data is information on the database. GIS database has two types of data vector and raster, and attribute data. Recording of GIS data sets is known as metadata. It has information such as who created the information, when, how to contact them, explanations of any code of attribute data and coordinate systems.
GIS software is essential in data analysis. It comes in three forms GIS application package, component GIS software, and web GIS software. The GIS application package is the best for geo- spatial editing and analysis. Web GIS software creates interactive maps that can be searched using web browsers.
Dr. John Snow was the first man in the world to conduct a geo-spatial data analysis in London in 1984. This was after a section of people in Soho Town died after a cholera outbreak. Through analysis and some few interviews he conducted. He was able to trace the cause of the disease and predict areas where it was likely to spread. The source was contaminated water in major pumps. The government used the data to prevent the spreading of the disease.
GIS uses some of the methods of data analyses such as data visualization. This comes in the form of cells, points, and lines to represent cities, roads, and streams. It is easy to identify areas with these features on GPS. Tabular data is represented using tables. For instance, if a school is located at a certain point and it is represented using a dot clinking on it will show all the attribute information in table format.
In conclusion, data analysis is the collection, interpretation, and evaluation of data for better decision making. Geo-spatial data is a type of information with a geographical aspect. It is analyzed using GIS. GIS is a technology-based system that incorporates geo-spatial information and tabular data to map and analyze information using analytical and statistical methods.
It is used for identifying disaster-stricken areas, humanitarian crisis, climate change, and predicting areas prone to catastrophes. Information garnered from GIS is crucial for the management of government institutions, businesses, and organizations.
Notably, in order for geo-spatial data to make sense it has to be interpreted. Similar to other types of quantitative data, these data are analyzed by the use of statistical tools. This is to say that in order to understand geo-spatial distribution, spatial correlations as well as association then one has to utilize various statistical tools so as to make sense of such data. It is worth to note that there are two major types of statistics that are used to analyze geo-spatial data. These are namely, descriptive and inferential statistics. When using descriptive statistics the goal is normally to describe the traits of sample of a certain geo-spatial phenomenon. On the hand, when using inferential statistics the goal is to come up with generalizations of the given geo-spatial population from which the sample under analysis has been drawn. It then follows that geo-spatial data statistical analysis can be quite complicated.
One of the key concepts that researchers are normally interested in when conducting this type of research is spatial interaction. Here, they look at how activities in one place are related to those in another place. It is worth to note that there is a formula that uses characteristics of different mass sites and the distance between them to predict such interaction of activities. It is therefore possible to understand things like; spread of information, transport and economic activities of a certain region by focusing on spatial interaction. This is therefore one of the major ways through which geo-spatial data statistical analysis makes data meaningful to information consumers.
Moreover, spatial correlation is yet another area that researchers focus on when conducting geo-spatial data statistical analysis. The reason as to why spatial correlation is important is because it shows the extent to which different geo-spatial phenomenon are related to one another. Once such a relationship is identified, it is possible to predict how a given phenomenon will change when another phenomenon or variable that is related to it is altered. Furthermore, when conducting this type of analysis you might also be required to focus on spatial association and testing of various hypotheses using geo-spatial data. If you are feeling that conducting this type of analysis is too much of a burden to you then you should be sure to contact us. This is because we have experts who guide students in conducting geo-spatial data statistical analysis at very pocket friendly prices. We assure you that we are a trustworthy company.
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