In the previous article, we tend to brought up the sources required to collect knowledge for any geodata mining. during this article, it’s worthy to say some tools, methods, and relationships existed between geodata, so as to form the method of Geodata mining easier and simpler. Initially, you must grasp that each one the tools and strategies delineated below square measure compatible with GIS, that is that the most holistic software package particularly with the external extensions for geodata mining. So, let’s plow ahead seeing the foremost vital relationships and rules govern the method of geodata mining, that crystal rectifier United States to conclusions concerning geodata.
Spatial Relationships Between Geo data
These relationships were galvanized by the science of statistics and aim to elucidate what square measure the common points between differing types of spacialgeodata. to create it a lot of specific, let’s suppose that we’ve 2 spacial objects A and B with a topological relationship. Those things square measure consisted of a group of points and will be overlapped, disjoint, intersected or contained one within the different.
Disjoint: The case when there are no points of an item contained in the other
Overlaps & Intersects: Includes at least one common point of A and B.
Equals: Demands all points of A and B to be in common in order to be equals.
Covered by or inside or contained in: One of the two items may be smaller and included in the other. All the points of the smaller are contained in the bigger, but points of the bigger one may be outside the other.
Covers or contains: In this case, the item A may contain B, if B item is contained in A.
There area unit a lot of relationships between geodata like the direction of the item and also the distance, however it’s tougher to be determined. However, it’simportant to grasp these parameters, thanks to higher identification and as a result to finish up in higher conclusions per our analysis.
Geodata Mining Tools
There are many tools helping us in the process of geodata mining. All of them are based in a programming language.
Open source geodata mining tools
DBMiner & GeoMiner: Specialized in geodata mining and query language.
GeoDA: Another open source tool based on Python language, used for statistic spatial relationships and spatial regression.
Weka-GDPM: Based on Java language supports many usual operations of geodata
Descrates: It is a tool intended to analyze the geodata, visualize and display the result. Operates with Python language.
Programming languages for geodata mining
There square measure many programming languages specialised and fully appropriate for geodata mining, like Python, C, FORTRAN, R language etc. every language has its benefits and downsides and it’s used for various scopes within the same method of geodata mining. Moreover, as we tend to saw on top of, all the tools created for geodata mining operate in one among those programming languages, therefore we tend to find yourself that the programming languages is that the initialand also the last step of the sure-fire geodata mining, further as we are able to use them self-contained, or in an exceedingly completed software system like GIS for a lot of economical results.
Techniques for Mining
There is a categorization of the many techniques we tend to use for geodata mining, counting on the results we would like to require. the primary class is that thedescriptive geodata mining whereas the other is remarked the prophetic geodata mining. The descriptive mining has the flexibility to explain capabilities and behavior of a dataset. it’s a straightforward thanks to perceive general properties and find yourself in superficial conclusions. On the opposite hand, if we would like to traveldeeper to the dataset we are able to use prophetic geodata mining. this method is a lot of sophisticated because it relies on pc algorithms that decide to fabricate patterns between geodata, that semiconductor diode United States of America to more predictions regarding the setting. it’s obvious that a prediction model could also be wrong particularly once we point out pc algorithms. For that reason, human intervention is critical. However, these days the algorithms area unit thereforerefined, and that they take into consideration several parameters, that it’s nearly not possible to search out a blunder.
Furthermore, prediction geodata mining is split into four subcategories as shown below:
Clustering and outlier detection
Spatial items or geodata, in other words, are grouped in categories called clusters. We create many clusters with geodata from the same dataset, where each cluster includes geodata with many similarities, while between the clusters there are many divergencies. In that way, we deal with big datasets, but we can discover rules only for the non-spatial properties of geodata.
Association and co-location
This method is used to find spatial rules connecting geodata between them. In this case, we search not only the non-spatial properties but every single spatial feature. Moreover, “association” technique has the ability to make a research on all the already discovered rules among other Geo-data sets to find if one or some of the rules are suitable for that case.
According to the classification technique, we create a possible model and then we analyze geodata according to that model. We could say that it is the inverse process, where the help of the human factor led to exclusion of other cases and patterns in order to find the right one.
Trend detection demands a previous existence of geodata a few specific spacial object or location, so as to search out changes over the time. Therefore, we tend to point out method that is specialized within the way a spacial feature amendment, reference to the time, distance, size, quality and different parameters.
To learn more about geodata mining and GIS you can visit UIZ webpage or call us at +49-30-20679115.