In a world where every asset, employee, and event has geographical coordinates, table-based management becomes ineffective. Geoanalytics is a technology for converting arrays of spatial and attribute data into visual maps and graphs. It allows you not just to see where something is, but to understand why it is happening there, to identify hidden patterns and predict the development of events.
In order for Digital Shadow to be not just a data warehouse, but a working management tool, geoanalytics is necessary. A digital shadow is a dynamic, constantly updated digital replica of an enterprise that collects data from sensors, GPS trackers, and accounting systems. Geoanalytics also serves as a bridge between this huge amount of information and the person making the decision. It transforms complex data streams into understandable visual images — maps and graphs — allowing you to instantly assess the situation and act proactively.
The cGIS Pro platform provides flexible ways to download data for analysis. Analytical layers can be built based on data obtained in different ways:
1. Search Results: The classic scenario is when you perform a search on existing data in the system and send a sample for analysis.
2. Preview and Import: You can download data from external files (CSV, GeoJSON, SHP, Parquet....). CGIs Pro allows you to open a file in preview mode, instantly visualize it on the map and immediately apply geoanalytics tools.
3. Saved datasets: Analytics can be built based on previously saved data samples. You can hide, show, or completely clear an already created layer from the map directly from the Geoanalytics menu, without losing the original data sample.
At the same time, an important option is available: you can build a layer based on all found records, or only on the data displayed on the current table page.
The Geoanalytics tool of the cGIS Pro platform allows you to build 9 types of analytical layers, each of which solves its own unique task.
Purpose: Visualization of the density and intensity of events, where foci of concentration are important, not individual points.
Key settings: Blur radius, smoothing coefficient, aggregation function (sum, average), palette.
Example (Digital shadow of a factory): Data from machine vibration sensors is displayed on the workshop map. The heat map highlights “hot zones” in real time — areas with an abnormally high frequency of vibrations that are harbingers of a breakdown. The engineer sees the source of the problem and sends a team for preventive maintenance.
Purpose: To display each individual object, where its characteristics (performance, status, etc.) are encoded through the color and size of the dot.
Key settings: Indicator for color, indicator for radius (or fixed radius), radius multiplier, color distribution (symmetrical/asymmetrical), popup settings.
Example (Mining Machinery Management): A fleet of dump trucks is displayed on the quarry map. The color of the dot indicates the status (green — in motion, red — malfunction), and the size of the dot indicates the current load in tons. The dispatcher identifies “traffic jams" and idle equipment at a glance.
Purpose: Aggregation of data by square cells for an objective comparison of uniform areas of the territory.
Key settings: Cell size, aggregation function (sum, average, min, max, quantity), 3D height extrusion, color distribution.
Example (Mining industry): The mining company has exploration drilling data for the future career — hundreds of well points, for each of which the percentage of copper content is known. Displaying them as separate dots is not informative. Using a grid layer, the engineer divides the entire territory of the quarry into 50x50 meter squares and calculates the average copper content for each square based on the wells that enter it. The result is a visual schematic map, where rich ore bodies are highlighted in red and empty rock is highlighted in blue. Such a map is the basis for planning mining operations, calculating reserves and optimizing production.
Purpose: Improved aggregation of data on hexagonal cells, which are ideal for statistical analysis, as they eliminate the distortion of the square grid.
Key settings: Similar to the grid layer (cell size, aggregation, extrusion).
Example (Industrial Ecology): Air quality sensors are placed around the plant. The hexagonal layer aggregates the average values of the analyzed values. The cells, colored red and “squeezed out” in 3D, show the zones where the maximum permissible concentration is exceeded, allowing you to simulate the spread of emissions.
Purpose: Thematic coloring and 3D extrusion of ready-made polygonal objects (workshops, cadastral plots).
Key settings: Indicator for color, indicator for extrusion height, height multiplier, color distribution.
Example (Asset Management): The industrial site map is divided into landfills (workshops, warehouses). The layer is colored depending on the energy consumption. High red polygons indicate the most energy-intensive facilities, helping to identify the potential for optimization.
Purpose: Visualization of the characteristics of linear objects (roads, pipelines, conveyors).
Key settings: Indicator for color, indicator for line width, width multiplier.
Example (Pipeline Monitoring): Digital shadow of an oil pipeline. The color of the line depends on the diagnostic data (green is normal, red is critical wear), and its thickness depends on the pressure in the pipe, which allows you to plan repairs.
Purpose: Classification of objects according to specified rules (ranges of values or exact categories), and not according to a gradient scale.
Key settings: The distribution mode (by range or by exact values), setting the rules and the corresponding style (color, size, width).
Example (Maintenance Management): The equipment park is displayed on the map. With the help of thematic mapping, machines are colored according to their status: green (completed), yellow (required soon), red (overdue). The manager instantly sees the problematic assets.
Purpose: High-performance aggregation of huge amounts of data using a hierarchical tile system (quadkeys). It is ideal for analyzing national-scale data.
Key settings: An attribute with a quadkey index, an indicator for color and extrusion.
Example (National Logistics): The Postal Service tracks millions of parcels. The tile layer aggregates this data. On the national scale, flows between regions are visible. As you approach the city, the grid details, showing the load on the sorting centers.
Purpose: Visualization of data already aggregated by Uber's global hierarchical geospatial H3 indexing system.
Key settings: An attribute with an H3 index, an indicator for color and extrusion.
Example (Network coverage analysis): The telecom operator analyzes the signal quality calculated in advance for each H3 cell. The layer instantly displays low-coverage areas across the country at any level of detail.
The connection of the map with time and statistics is the key to understanding the dynamics of processes. If the data has a date/time attribute, it can be activated for any analytical layer by adding a timeline to it. This allows you to “play” events in time, like a movie, by filtering the data on the map and graphs in the selected time window.
Usage example:
The digital shadow of the warehouse. The animation of loader tracks per shift on the timeline shows that “traffic jams” regularly form in the acceptance area at 14:00. This is a direct indication of the need to optimize the schedule.
The graphs are built in direct connection with the map. By filtering the data on the map or timeline, you can instantly see the changes on the chart. The following chart types are supported: linear, areal, dotted (for finding correlations), columnar, and circular. The key settings are: attributes for the X and Y axes, aggregation functions (sum, average, quantity), criticality, the ability to add a second Y axis to compare disparate indicators on the same graph (for example, the number of failures and temperature).
Usage example:
To investigate equipment downtime, a heat map is used, which shows the concentration of failures at conveyor No. 3.
The timeline shows the peaks of failures on Tuesday and Friday.
The line chart confirms the peaks. We add a second Y-axis with temperature data from the nearest sensor. The graph shows a clear correlation: the peaks of failures coincide with the peaks of temperature.
Conclusion: the problem is not in the conveyor, but in the ventilation system. The analysis took 15 minutes.
The introduction of geoanalytics is not just a transition to a more modern type of reporting. This is a fundamental change in the management approach, which brings measurable economic and operational benefits.
Key benefits of implementation:
1. Cost reduction: Through preventive maintenance, logistics optimization, and point-based resource allocation.
2. Increased operational efficiency: Rapid identification of bottlenecks and anomalies in production processes reduces downtime and speeds up cycles.
3. Enhanced security: Real-time spatial risk monitoring helps prevent incidents rather than reacting to their consequences.
4. Sound strategic planning: Decisions about development, modernization, and investments are made based on objective, visualized data, not intuition.
Let's look at how these benefits are realized in the real-world tasks of key industries using specific geoanalytic tools.
Objective: To reduce accidents and downtime of underground self-propelled vehicles due to the unsatisfactory condition of the roadway in the mine. Solution: GPS/GLONASS tracking data from all cars is uploaded to the system within a week.
Tool: A heat map is built at the points where the speed of the equipment fell below 2 km/h. The map instantly highlights “hot zones” — areas with holes, heavy flooding, or rock blockages where drivers are forced to constantly brake.
The result: Instead of intuitively patching roads, repair crews receive an accurate map of the most problematic areas, which allows them to purposefully use resources and significantly reduce the cycle time for ore transportation.
Objective: To optimize the repair and modernization program for 0.4 kV distribution networks in order to reduce the number and duration of outages for consumers. Solution: An archive of data on all emergency outages for the year is being analyzed.
Tools:
1. Thematic mapping is used to color the layer of points of accidents due to their cause (red is an open wire due to aging, blue is a short circuit at a substation). This immediately shows whether different areas are suffering from different “diseases”.
2. The Grid layer aggregates the total duration of outages in each cell (quarter).
The result: Management sees on the map not just “where it was turned off,” but “where it was turned off the longest and for what reason,” allowing investments to be directed specifically to those areas and types of equipment that create maximum problems for consumers.
Objective: To optimize the maintenance process of a huge fleet of in-house railway wagons in order to avoid the use of expired maintenance units.
Decision: In real time, the locations of all wagons are displayed on the plant's map, for each of which the date of the last maintenance is known.
Tool: The thematic mapping layer colors each car (point object) according to the service status: green (60 days overdue).
The result: The dispatcher sees the operational situation on the map. If he notices a cluster of “red” wagons in the sludge park, he can send a mobile crew there to carry out maintenance, instead of searching for these wagons throughout the plant or, even worse, allowing them to be loaded.
Objective: Quickly identify systemic defects in new models of vehicles operating in different climatic and road conditions. Solution: Data is collected on all warranty requests to service centers across the country.
Tool: A HeatMap is created based on requests related to the failure of a specific node (for example, “turbocharger failure").
The result: If a bright cluster of failures appears on the map in one region (for example, in northern Siberia), this becomes irrefutable evidence for the design bureau. The problem is not an accidental marriage, but a systemic defect that manifests itself in low temperature conditions. This allows you to immediately start refining the node and avoid massive reviews and reputational losses.
These examples prove that geoanalytics in cGIS Pro is not an abstract technology, but a powerful platform for building the digital shadow of your enterprise, allowing you to turn accumulated data into the most valuable asset: an informed and timely decision.
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