Introduction To Geospatial Data Analytics
Geospatial Data Analytics represent objects, events, and other features according to the location or near the earth’s surface. The geospatial data mix up the location and attribute information with temporal information. Geospatial data is a time-based type of data associated with the earth’s particular location. The location given by the geospatial data is static or dynamic. The locations could be moving vehicles, earthquake events, or children living in poverty.
A large of spatial data is included in the geospatial data extracted from many different sources. Geospatial data is included in various formats, including census, weather, cellphone, and social media data. Geospatial data is most useful when the data is shared, found, analyzed, and used for different purposes by various typical businesses.
Types of data analysis
Data analytics is one type of data science. Data analytics is done for various purposes. The data analysis aims to extract useful information by processing, cleansing, transforming, and modeling the data. There is a type of data analysis they are differentiated based on its difficulty.
- Diagnostic analysis- in the data analysis, the question of how this happens is answered by diagnostic analysis. The using statistical analysis answers the questions. The data analysts use diagnostic analysis to find out patterns in the data. The past analysis is used for present scenarios to resolve problems.
- Predictive analysis – in the data analysis, the predictive analysis answers the question of what will happen. Based on old and present and predict the future predictions.
- Prescriptive analysis – the prescriptive model mixes up all the data insights gained. There need various insights from one of the insights. The authoritarian model cannot be worked.
- Statistical analysis – what happened is answered by the statistical data analysis. This analysis is used for the dashboard’s transformation, interpretation, and model presentation.
- Text analysis- text analysis is also called data mining. The consistent patterns are being analyzed by data mining and database tools. Text analysis converts the raw data into useful information. This type of data analysis is the most straightforward form of data analysis.
Let us understand the uses of geospatial data.
- Mapping– is the most used type of geospatial data used to visualize the area that the data describes, for example- building footprints, transportation routes, or precisely drawn maps. These are made based on the location data, which is very powerful. It is helpful that the traveler or people would know their way in and around a specific locality.
- Site selection– suppose you want to open a shop in a locality where the customer will visit your shop. Now you also want to tell the customer about the things which have closed near your shop. Geospatial data will help you with that. As he geospatial data while selecting and deselecting the retail shop, they ended up e something to take into consideration. Like where is your current location of the shop, the competitor’s shop in the locality, and what is the transportation available near the shop? These things are taken into consideration by the geospatial data.
- Risk assessment – the insurance companies are the ones who are making use of geospatial data these days. To develop liability frameworks for buildings, insurers should know a number of geospatial traits. This includes where a building is, how much space it is made up of, and how close it is to surrounding buildings. These specifications let them know how a building is to things like damaging weather or even damage indirectly by indirect sources (e.g., a spreading fire, vehicle accident.
Geospatial Analytics
Geospatial analytics collects, combines, and visualizes different types of geospatial data. The model of geospatial data analytics is to model and represent the object and how the people interact within the space. It also makes assumptions based on trends between types of places. Geospatial analytics put the data into a more available format by announcing the elements of space and time.
Information that is challenging to get out of reading lines in a table or spreadsheet becomes much simpler to understand with the help of a visual representation of what the world looks like. This helps people more easily find patterns such as distance, proximity, the density of a variable, and other relationships. Summarizing geospatial analytics is about determining what happens, not only where, when, and why it happens at a particular place and time.
Now let’s find out the benefits of geospatial analytics. In a large number of raw data, the data analytics makes the trend in vitality representable related to space and time.
- Recognizing the spatial patterns and trends – some relationships and trends are not understood in the absence of where the trends are occurring.
- More chances for segmentation- when the location is added to the part of the geospatial analytics, you can start to filter and segment the basis of the geography, which can make your geospatial data analytics more detailed.
- The better decision by better predictions – studying the phenomenon over time in the context of a specific location which tells about the why, where, and when this happens. The geospatial analytics when, why, and where it happens.
Geospatial Analysis
Most businesses or companies use excel spreadsheets to store data in rows and columns, which does not generate any insights from the data. Here comes the geospatial analysis in the picture. Geospatial analysis is used for gathering, storing, and analyzing the data to generate valuable insight from the data provided. Geospatial analysis is a powerful and immensely used concept for businesses around the globe and has poured their idea in front of the world. The maps make the work easier as they help find the patterns kept in the spreadsheets.
Geospatial analysis is used in various aspects, such as.
Sales and Marketing
Geospatial analysis is used for the customer segments for improved targets by sales and marketing teams. Information-based targeting will lead to a maximized customer base.
Transportation and Logistics
The location-based planning of transportation and Logistics can do cost cutting which is efficient utilization of resources of the organization. Geospatial analysis helps vitally in supply chain management.
Situational intelligence
situational intelligence, through map-based visualization, businesses understand situations better and, in turn, plan accordingly. For instance, why is there an increase or decrease in customer response in a particular region? Is it due to some festival or holiday season?
Risk Analysis
Risk analysis is similar to insurance companies, which can get insights into the areas where they are overexposed.
Strategic growth and optimization
The Geospatial analysis helps organizations to understand the regions where they can maximize their operations. The maximization involves various factors like time, effort, and money so that they can be more insightful.
Satellite Imagery
the most popular Geospatial analysis use case. Every time you log in to Google Maps to know about the location or how much time you will take to reach your destination, it is because of the Geospatial analysis
Conclusion
The article consisted of the types of data analysis and how they work. Geospatial data is a powerful concept and has changed the game of space and geography. As it makes it easier for the user to find a location or get an insight about the locations etc., the two branches of geospatial data- geospatial analytics and geospatial analysis- have different functions and uses. They both are helpful in different aspects according to their functions.
