RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 16, ES6004, doi:10.2205/2016ES000587, 2016

GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform

A. A. Soloviev1,2, J. I. Zharkikh1, R. I. Krasnoperov1, B. P. Nikolov1, S. M. Agayan1

1Geophysical Center RAS, Moscow, Russia

2Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences (IPE RAS), Moscow, Russia


This paper presents software solutions for integration of geoscience data and data processing algorithms based on the Discrete Mathematical Analysis (DMA) in GIS environment. The DMA algorithms have been adapted and implemented within the ESRI ArcGIS software as geoprocessing tools and combined into a single set of tools named "Clustering". This set can be used along with the standard ArcGIS geoprocessing instruments. The tools of the "Clustering" set have also been published on the GIS-server as geoprocessing services providing powerful analytical functions via the Internet. This paper gives a brief outlook of the geoprocessing tools preparation techniques. The results of DMA-based geoprocessing tools' application to geophysical data are also discussed.


Efficient processing and analysis of georeferenced data require adequate instruments, which nowadays are provided by geoinformation systems (GIS), including a great variety of geoprocessing tools. In some cases, specific tasks require implementation of algorithms based on latest advances in data mining techniques. Modern GIS software such as ESRI ArcGIS allow designing new geoprocessing tools based on original mathematical algorithms, using the Python or ModelBuilder program languages. The newly designed tools, which are called "custom tools", along with the standard ones broaden the software geoprocessing functionality. Custom tools can be unified into a single package, which is easily transferrable. They can also be published on a GIS server enabling its further use in web applications, thus becoming accessible to a wide community. The presented results are the part of the ongoing development of the intellectual GIS for geoscience data analysis, carried out at the GC RAS [Berezko et al., 2009a, 2009b, 2010, 2011a, 2011b; Beriozko et al., 2007, 2008, 2011; Gvishiani et al., 2007; Krasnoperov and Soloviev, 2015; Krasnoperov et al., 2012; Nikolov et al., 2015; Soloviev et al., 2007]. This paper describes the results of adaptation of clustering algorithms based on the Discrete Mathematical Analysis (DMA) approach for their integration into GIS environment as new geoprocessing tools. The developed tools have been combined in a geoprocessing package "Clustering" implemented as the part of the geoprocessing toolbox set of the ESRI ArcGIS platform. The instruments of the created toolbox have also been published as geoprocessing services on the GIS server. The advantages of this approach are discussed in [Nikolov et al., 2015].

Geoprocessing Algorithms

Mathematical algorithms used for the creation of geoprocessing tools and services are the part of the DMA approach developed at the Geophysical Center of the Russian Academy of Sciences (GC RAS). DMA represents a set of algorithms unified by a single formal basis aimed for studying and analysis of multidimensional arrays and time series. The series of DMA algorithms have been successfully implemented for analysis of various geological and geophysical spatial data [Agayan and Soloviev, 2004; Agayan et al., 2010, 2011, 2014a, 2014b, 2016; Bogoutdinov et al., 2010; Gvishiani et al., 2002a, 2002b, 2008, 2010, 2011, 2013, 2014; Mikhailov et al., 2003; Soloviev et al., 2005, 2009, 2012a, 2012b, 2013, Soloviev et al., Estimation of Geomagnetic Activity..., Annals of Geophysics, 59(6), in press; Widiwijayanti et al., 2003; Zelinskiy et al., 2014]. In the framework of the current research the following DMA-algorithms have been adapted for implementation as geoprocessing tools: "Discrete Perfect Sets" (DPS), its modification (DPSm), "Monolith" and "Rodin-2".

The "DPS" clustering algorithm was created for separation of dense regions with a certain density level $\alpha$ in a set of point features. Two main parameters that can be adjusted by the user are $\omega \;(\omega <0)$ for determination of closeness radius and $\beta \in [0,1]$ for determination of density level. Determination of dense areas in the modified "DPSm" algorithm is performed generally in the same way as in the conventional DPS. The difference is in the method of determining the density of points [Agayan et al., 2011, 2014a].

The "Monolith" algorithm is aimed at recognition of dense subsets of the elements in metric spaces with accordance to the level of density. The result of the algorithm application may contain isolated points. The algorithm parameters are the level of density $\alpha \in [0,1]$ and a negative parameter $\omega <0$, which is required for determining the proximity radius.

The "Rodin-2" algorithm was designed for recognition of dense regions in finite metric spaces. It is based on the Kolmogorov mean and "fuzzy comparison" constructions. The "Rodin-2" free parameters include the proximity parameter $p \in \Re$ and the threshold parameter $\alpha \in [-1,1]$ [Gvishiani et al., 2008, 2010; Nikolov et al., 2015].

Integration of Algorithms Into GIS

The ESRI ArcGIS platform has been chosen as the GIS environment for integration of the DMA clustering algorithms as geoprocessing tools. It is an advanced applied geoinformation software platform, which provides the following functions: support of the most of the conventional GIS standards and formats; compatibility with other platforms, databases, development languages and applications; efficient server-based solutions.

The DMA clustering algorithms "DPS", "DPSm", "Monolith", and "Rodin-2" were programmed using Python 2.7, which is one of the main development languages used in ArcGIS. It is a non-proprietary language, which has many additional libraries that allow to expand significantly its functionality [Zandbergen, 2013]. Python is a popular language among GIS developers since it provides a large number of well-documented classes and functions, numerous specialized libraries (ArcPy, NumPy, etc.), ease of loading and running of scripts, a great variety of examples. Advantages of the Python language are:

For geodata management a special library ArcPy was used. It includes the majority of GIS functions and facilitates greatly the process of scripts' compilation. ArcPy module allows to convert and analyze georeferenced data and automate mapping functions using Python scripts.

Another Python library that was used is the NumPy library, which is a free and powerful equivalent of MATLAB [Lutz, 2010]. NumPy is a fundamental package for scientific computing, which provides a great variety of capabilities such as operations with multi-dimensional array objects; tools for integrating C/C++ and Fortran code; capabilities of linear algebra, Fourier analysis and many others [NumPy: [web site] URL:].

Initially the clustering algorithms were programmed as Python scripts with certain functionality which were later combined as an ArcGIS set of geoprocessing tools. This allows using the developed tools along with the standard ones. Creation of a geoprocessing tool requires an initial script (in this case, an algorithm programmed in Python), an empty geoprocessing package, and the exact description of the parameters and the order of their execution in the compiled script.

On the next step, it is required to add all the developed scripts into the empty toolbox as separate tools. To do this the paths to each of the script's files are specified as well as the input parameters for each of them. On this stage, it is very important to designate the order in which the parameters are specified and determine their type. The main types, which are used for the input parameters of these algorithms are "Layer" (reference to a source of point data, such as a shapefile or geodatabase feature class), "Double" (any floating point number), "Long" (integer number value) and "Workspace" (geodatabase or folder that will contain the result of the geoprocessing). Finally, the toolbox (*.tbx) file, containing the clustering algorithms, is formed. It can be easily transferred via local computer networks or the Internet and distributed among users. After receiving a toolbox file, a user cannot change the tools' source code or their structure. It is available only for input of the tools' parameters while execution. To change the code of the tools, the user should know the password, set by the author of the toolbox.

Fig 1
Figure 1

To run the geoprocessing tools the user should utilize one of the components of the ArcGIS platform (ArcMap, ArcCatalog, etc.) with the ArcToolbox panel. Once the "Clustering" toolbox is available at the ArcToolbox panel (Figure 1), the user can apply its tools for specified geospatial data processing.

Fig 2
Figure 2

The "Clustering" geoprocessing toolbox has a simple, understandable and user-friendly interface that allows the user to run the required geoprocessing tool quite easily. After selecting the required tool a dialog window pops up. The appearance of the dialog window of the running tool is shown in Figure 2. The window provides fields for input of parameters, required for the tool execution (data layer path, free parameters, output layer path etc.), and a text field with a short description of the tool. The results of the tool execution are automatically stored in the specified location. If the user works in the ArcMap application the results are automatically added to the current map, and the user can view and analyze them along with the initial data.

Fig 3
Figure 3

After the execution of the algorithm a window with the status information and results pops up. It displays the values of the input parameters, data messages, the elapsed execution time, errors and warnings if they occur during the process (Figure 3a, Figure 3b).

All the above mentioned clustering algorithms are applicable only to point data, so in the input "Layer" field of the tool dialog window only data layers with point objects should be selected. If the user inputs incorrect data, an error message appears in the execution results window. An error message also appears if the user enters the tool parameter values that do not fit the specified requirements (e.g. free parameter is out of the range). The implemented geoprocessing instruments can be presented not only as toolbox packages for desktop software, but they also can be published on the GIS-server as geoprocessing services, which makes them available to the whole Internet community. The presented geoprocessing instruments were published on the GC RAS GIS-server as the part of the Centralized Catalog of Geodata Processing Algorithms (CCGPA). This subsystem is responsible for access to specific methods of geodata processing performed centrally on the GIS-server.

The principal advantages of this approach are [Lebedev and Beriozko, 2009]:

Application, Results, Experiments

Fig 4
Figure 4

The implemented geoprocessing instruments were tested on both synthetic and real spatial datasets. A synthetic point dataset was generated and imported as a layer in the ArcMap software. The instruments of the "Clustering" toolbox were applied to this layer with different free parameters' values. One of the examples of the "Monolith" tool testing is presented in Figure 4. The results of clustering were saved as new layers, which can be used in further data analysis.

Fig 5
Figure 5

The algorithms published as geoprocessing services were also tested in online mode using seismological data from the online GIS on Earth Sciences, maintained by GC RAS ( The results are presented in Figure 5.


Adaptation of original clustering algorithms ("DPS", "DPSm", "Rodin-2", and "Monolith") as geoprocessing tools and services broadens the analysis apparatus for efficient management of geospatial data. The created "Clustering" toolbox can be easily distributed among the GIS community via the Internet or other digital media. The programmed algorithms are also published online as geoprocessing services thus giving an opportunity to analyze geoscience data without using commercial desktop software. Further theoretical and practical research will focus on algorithms, capable to process not only point data, but also linear and polygonal objects.


This paper is based on the report presented at the international conference "Data Intensive System Analysis for Geohazard Studies" held on 18–21 July, 2016 in Sochi, Russia. This work was carried out in the framework of the project of the Fundamental Research Program of the RAS Presidium No. 4 "Strategic mineral resources of Russia: innovative approaches to forecasting, assessment and extraction".


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Received 24 November 2016; accepted 1 December 2016; published 13 December 2016.

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Citation: Soloviev A. A., J. I. Zharkikh, R. I. Krasnoperov, B. P. Nikolov, S. M. Agayan (2016), GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform, Russ. J. Earth Sci., 16, ES6004, doi:10.2205/2016ES000587.

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