Alexis Kestler
Location intelligence is rapidly emerging as a key catalyst empowering enterprises across industries to formulate strategies, plans and operations enriched with geographical context for sharper insights and improved outcomes.
Easy to use geospatial tools that process and visualize location data have brought spatial analysis capabilities from the realm of GIS experts to mainstream business users. This enables diverse teams to extract actionable intelligence, drive decisions and boost performance leveraging geospatial techniques tailored for their domain and use case.
As an aspiring data analyst keen to expand your skillset, it is highly recommended to start familiarizing yourself with location-based analytics since it is going mainstream. This primer article discusses popular geospatial tools and key concepts data crunchers should grasp to embark on this exciting journey!
Why Geospatial Matters
Let‘s first understand why organizations view geospatial prowess as a strategic priority:
Soaring Data Volumes – Emerging technologies like IoT, GPS, satellites and mobility are causing exponential growth in geospatial data available to enterprises. Industry reports forecast location-based data volumes to grow over 100% annually compared to 40% for conventional data.
$500 Billion Value – Geospatial analytics revenue across hardware, software, data and services is projected to become a half trillion dollar industry by 2023 as location intelligence gets embedded into mainstream business and technology fabric.
Mainstream Adoption – Over 80% of business data possesses some location attribute. Companies across sectors realize geography provides vital context for use cases spanning targeted marketing, optimized logistics and predictive modeling.
Improved Outcomes – Location enabled operations drive significant upside across customer experience, supply chains, risk management and product innovation based on data-driven geographical insights.
Think Spatially
So for aspiring analysts seeking to future proof skills, it is prudent to start incorporating location perspective within your data wrangling workflows. Depending on use case complexity, you can apply varying degrees of geospatial techniques – from basic mapping to advanced spatial modelling.
Let‘s explore popular tools to navigate this exciting domain!
Geospatial Data Fundamentals
Since your analytics success hinges on input data quality, first understand key spatial data types and formats you may encounter:
| Format | Description | Common Uses | Limitations |
|---|---|---|---|
| Vectors – Points, lines and polygons representing discrete real-world features | Highly accurate modelling and computation | Complex representation of features | |
| Raster – Grid of cells representing a geographical area | Ease of visualization and analysis | Less flexibility in computations | |
| Point Cloud – Massive collection of geolocated points in 3D space | Ultra realistic digital reconstruction | Complex processing needs |
In addition, geospatial engines process location enriched tabular data from sources like sensors, databases and spreadsheets containing details like timestamps, city names, addresses, zip codes etc. that connect information to places on the earth‘s surface.
What Geospatial Tools Empower You With
Whether you seek to create a snazzy map, simulate a viral outbreak or optimize inventory allocation across a supply chain network spanning continents – geospatial tools equip you with specialized capabilities to process geographical data and extract actionable intelligence tailored to your use case requirements.
Here are some common feature sets:
Data Ingestion – Support ingesting spatial data from files, databases, cloud storage, web services and sensors
Geocoding & Geoenrichment – Convert descriptive locations like addresses, place names into geographic coordinates with rich attributes like zip codes, demographics etc attached
Records Linkage – Link and fuse different spatial, non-spatial, internal and external data sets using location fields
Spatial Analysis – Domain-specific analytical techniques like proximity analysis, geofencing, location allocation applied over geographical data
Predictive Modelling – Project likely scenarios related to risk, natural calamities, disease spread, population migration, vehicle traffic etc over regions
Routing & Navigation – Find shortest or cost optimal routes and navigation markings between origin and destination points or geofenced areas
Advanced Overlays – Combination of multiple map layers with custom aggregation logic to uncover trends – think heat maps!
3D Modelling – Photorealistic modelling of landscapes, infrastructure and physical assets with accurate geospatial positioning
Visualization – Interactive vector and raster maps, dashboards and analytics apps with compelling cartographic polish
This diverse set of capabilities makes location intelligence indispensable for modern enterprises. Now let‘s explore leading options you have for geospatial tools…
Top Geospatial Tools for Aspiring Analysts
When selecting a solution, opt for one aligning well with in-house skill sets that can grow with your analytical sophistication over time.
1. QGIS
QGIS is the go-to open source option for analysts seeking free access to professional grade GIS capabilities. With over 500 analysis, data conversion and geometry engines, it offers unparalleled functionality considering its zero license cost. Python savvy users can code custom spatial logic and applications.
However, expect a steep learning curve mastering its intricate workflows beyond basic visualization. Dedicated community support somewhat offsets lack of handholding available in commercial tools.
2. ESRI ArcGIS Suite
This venerable suite from geospatial leader Esri provides a layered set of capabilities catering to analysts at different skill levels. ArcGIS Online and ArcGIS Dashboards make web maps, spatial analysis and dashboard creation accessible via drag and drop workflows. Expert users can leverage heavy duty advanced analytics and geosciences tools via ArcGIS Pro.
Expect licensing costs running into thousands of dollars considering wide deployment across large teams. But the refined ergonomics, polished visuals and tight cloud integrations enhance productivity for complex use cases.
3. GeoDa
GeoDa focuses specifically on spatial statistical analysis techniques like cluster analysis, regression modelling allowing analysts to uncover geographic/geometric patterns within data. Researchers can dynamically visualize statistical computations like autocorrelations by overlaying them as heat maps onto maps.
The open source tool may appeal especially to academically inclined analysts. Focus is more on geo-visualization of data science outputs rather than conventional GIS analysis.
4. CARTO
CARTO provides a cloud-native development suite allowing analysts to rapidly transform location data into business centric analytical apps and dashboards. With just point and click, you can execute spatial joins on billions of rows, create location defined cohorts through data segmentation, analyze trends through heat maps optimized for lightning fast visualization leveraging vector and raster map data.
Pricing tiers based on number of rows and features analyzed may climb high for large enterprises. But pre-built connectors and APIs simplify integration with modern cloud data platforms.
5. Mapbox
Mapbox specializes in rendering immersive map visualizations programmatically for embedding within websites, applications and analytics dashboards by tweaking a rich array of customization options to control interactivity, themes, labels, terrain etc. Integration libraries are available for leading visualization tools like Power BI, Tableau and Excel.
The tool allows analysts to enhance traditional reports and analytics with location context through slick dynamic map overlays. Mapbox charges based on monthly active users and map views which may limit large scale adoption.
6. Fulcrum
Fulcrum shines in simplifying field data collection initiatives crucial for many geospatial analytics use cases. Their mobile apps and intuitive form building dashboards eliminate need to build custom mobile apps. Location tracking, media attachment capture, routing and status notifications further optimize field workflows.
This ready platform can significantly accelerate analytics projects involving in-person surveys, site inspections and asset monitoring work compared to custom app development. Subscription charges apply based on number of applications, data volume and users.
Maturing Your Spatial Analytics Capabilities
Hopefully this primer helps appreciate why geospatial prowess should be an extension of modern analytical skillsets. Here are few quick tips to advance your location data analysis journeys:
Think Geo First – Explore if existing data assets contain underleveraged spatial attributes like location ids, addresses etc. providing geographical context.
Map The Journey – Institute a roadmap focused on phased geospatial adoption across people, process and tools considering in-house maturity level.
Mix And Match – Blend geospatial analysis techniques with conventional methods like machine learning, data mining for multifaceted insights.
Collaborate Across Functions – Seek opportunities to support location-based analysis needs of business teams like marketing, logistics and real estate.
Upgrade Skillsets – Attend geospatial coaching, certifications and hackathons to complement workplace exposure.
The democratization of geospatial analytics through easy-to-use tools is a boon allowing technically inclined analysts to support wider business objectives leveraging location intelligence without deep GIS expertise.
So go ahead, spread your data wings spatially!
