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Case Study
Unifying Data Sources: Resolving Locality Matching Challenges for Actionable Insights
Scattered data sources didn’t align by locality, impeding analysis. Our custom GIS solution with a geography API and visual dashboards consolidated accurate data for reliable decisions.
FASTER DATA MATCHING BY
70%through automated locality resolution
COMPLETE DATA CONSISTENCY
100%across sources and regions
INCREASED INSIGHT ACCURACY BY
40%using standardized locality polygons
Project Overview
Client:
GIS platform
Location:
Canada
Company size:
150 employees
Industry:
Geospatial hardware & software
Services:
● GIS data integration and normalization● Custom geography API development● GIS-based analytics pipeline design and implementation● Custom analytical aggregation tool development● Interactive dashboards and GIS data visualization● High-performance heatmap visualization for large datasets
Solution:
We solved the locality-matching issue by building a custom GIS powered by Statistics Canada polygons
Business Challenge
Have you ever faced the inability to operate disparate locality data?
Our client faced the issue of multiple data sources and inconsistent locality names that caused mismatches and blocked analysis. We had to align external data with each locality and display it in convenient dashboards and charts.
Misaligned Locality Data
Multiple external sources had to match locality names in the GIS system through GIS-based live data aggregation. In reality, different locality names from an external API caused persistent mismatches, so analytics failed to connect data to the right areas.
Limited GIS Data Aggregation
Locality-level aggregation from multiple sources was slow and inaccurate at scale due to the low capability of the existing tools. This restricted the opportunity to get deep and reliable analytical insights.
Low Data Usability
The collected data was difficult to operate because of poor visualizations and the lack of structured dashboards. Users struggled to analyze trends, make regional comparisons, and derive strategic insights.
Timing Matters
Solution Framework: Core Parts Shaping Further Implementation
■We established a solid foundation with trusted locality data. Our custom GIS solution utilizes reliable data from Statistics Canada locality polygons.
■Data alignment was achieved through API integration. Our custom geography API ensured consistent locality names and enabled seamless data collection and processing.
■Google Analytics couldn’t provide locality-level GIS aggregation. So, we built a custom analytics pipeline to process live geographic data accurately and support scalability.
Let’s Solve Your GIS Data Challenges
We identify the root causes of fragmented locality data and deliver a unified GIS solution that enables accurate analytics, clear visibility, and confident decision-making.
Solution Structure: Step-by-Step Project Execution
Custom Event Aggregation Service.
The created tool captures user and system events and adds GIS context in real time. The service validates locality data, structures records, and reliably stores them for further processing. The result is a scalable foundation for accurate geographic analytics and reporting.
Event Collection and Data Transfer
A C# API collected incoming events and validated locality references. Data was then sent through Azure Service Bus for smooth delivery and buffered into Data Lake for storage as raw records. This
preserves history and enables scalable, reliable processing for consequential analytics.Data Processing and Structuring
Python scripts in Azure Databricks transformed raw events into clean, organized locality datasets. Formats are fixed, duplicates removed, and data is summarized by locality. Then a .NET Core API delivers prepared data from storage, enabling a quick load of dashboards, maps, and reports without complex calculations.
Implementing Dashboards, Charts, and Maps
We worked with the client to tailor dashboards, charts, and maps to their specific needs. Based on the client’s objectives, the collected GIS data was turned into clear visualizations that highlighted trends and comparisons. This helps teams quickly understand locality performance without delving into raw datasets.
Interactive Regional Analysis Tables
We created interactive tables that allowed the team to view and explore locality data effortlessly. They could quickly filter, sort, and compare regions or cities, track user interactions, and identify patterns. The client has a convenient tool to determine trends and make decisions directly in the system.
Fast and Accurate Heatmaps for GIS Analytics
We added heatmaps to show where data is most concentrated in different areas. Since standard tools didn’t provide sufficient speed for processing large datasets, we created the heatmaps in advance as separate image layers for each zoom level. We placed them over the map, and the client’s teams could use color gradients to clearly see density and intensity. Thus, they could quickly identify hotspots and areas of concern. Map performance has improved, enabling the client to generate targeted reports for municipalities effortlessly.
Comprehensive Data Visualization Toolkit
We provided a full set of visual tools, including bar charts, line graphs, and pie charts, to make data easy to consume. Users could obtain a holistic view of changes over time, regional differences, and collected analytics. Together with tables and heatmaps, these visuals simplified the user experience, enabling quicker insights.
Legacy
Modern
Legacy
Legacy
Modern
Modern
☒ External geography API with inconsistent locality naming
☑ Statistics Canada locality polygons as a trusted data source
Legacy
☒ External geography API with inconsistent locality naming
Modern
☑ Statistics Canada locality polygons as a trusted data source
☒ Manual or unreliable locality-to-data matching
☑ Unified locality model ensuring consistent data matching
Legacy
☒ Manual or unreliable locality-to-data matching
Modern
☑ Unified locality model ensuring consistent data matching
☒ Google Analytics is limited for GIS-based aggregation
☑ Custom GIS analytics pipeline built for scale
Legacy
☒ Google Analytics is limited for GIS-based aggregation
Modern
☑ Custom GIS analytics pipeline built for scale
☒ Live heatmap calculations are causing performance issues
☑ Pre-calculated heatmap layers optimized for fast rendering
Legacy
☒ Live heatmap calculations are causing performance issues
Modern
☑ Pre-calculated heatmap layers optimized for fast rendering
☒ Fragmented dashboards with low analytical value
☑ Interactive dashboards with locality-level insights
Legacy
☒ Fragmented dashboards with low analytical value
Modern
☑ Interactive dashboards with locality-level insights
Quicker Reporting and Minimized Manual Analysis Time
A custom geography API, analytical tools, and dashboards automated processes and made them more convenient
Unified and Reliable Source of Geospatial Data
We established a centralized system to collect, process, and display consistent locality data
Clean Datasets Ready for Automation and Integrations
Standardized locality naming removed data inconsistencies and prepared it for further processing and analysis
Effortless Use and Quick Insights
Convenient dashboards comprehensively process raw data for easy detection of trends, patterns, and anomalies
Better Planning and Optimized Operations
Clear and intuitive visualizations enhanced efficient planning and operations, enabling proactive resource allocation
Faster and more Informed Decisions
Our custom GIS solution provided more reliable insights based on aligned and accurate locality data
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