Unleashing the Power of Geo-Spatial Analytics with Google Maps and QlikView

Geo-spatial analysis has emerged as one of the most powerful techniques in the data analyst‘s arsenal today. By combining the "where" with the "what", "when", "how" and "why", maps enable us to uncover location-based insights that might be near impossible to spot in tabular data alone.

The growing adoption of geo-spatial analytics is evident from market trends. According to a report by Allied Market Research, the global geospatial analytics market size was valued at $59.5 billion in 2020 and is projected to reach $158.8 billion by 2028, registering a CAGR of 12.1% from 2021 to 2028.

Several factors are driving this growth, key among them being the explosion of location data generated by mobile devices, IoT sensors, satellites and social media. Businesses across industries are realizing the potential of geo-spatial insights to optimize operations, inform strategy and deliver superior customer experiences.

If data visualization is storytelling, then maps let you tell the "where" of the story. And QlikView, with its integration with Google Maps, provides a compelling platform to narrate these spatial stories. Let‘s dive deep into the what, why and how of geo-spatial analysis with QlikView and Google Maps.

The Case for Geo-Spatial Visualization

While the value of geo-spatial analysis is quite apparent, let‘s solidify the case with some data. A study by researchers at the University of Amsterdam compared the effectiveness of spatial vs. non-spatial visualizations for analysis of geographically-tied data.

The experiment involved 125 participants performing tasks on a dataset of 35,000 worldwide terrorist attacks over a 40-year period. One group used a map-based visual while the other used bar charts, line graphs and other standard charts.

The results showed that the map users completed the analysis tasks with a 34% lower error rate and a 17% faster task completion time compared to the non-map group. The map visualization also scored higher on user satisfaction and engagement metrics.

This study illustrates what we intuitively perceive – that maps are a more natural and effective way to represent and analyze location data. Some key advantages of geo-spatial visualization include:

• Intuitively understand the geographic distribution and clustering of data points
• Easily identify spatial outliers, trends and patterns
• Overlay multiple location-tied variables using colors, shapes, sizes, heat maps, etc.
• Drill-down from macro to micro views and analyze at different zoom levels
• Combine geographic and non-geographic data dimensions for richer analysis

Spatial is Special with QlikView

QlikView is a widely used business intelligence tool globally, known for its associative data engine and rich visualization capabilities. While QlikView is used extensively for analyzing data across business functions, it really shines in the geo-spatial analytics space.

Some key features that make QlikView a compelling platform for geo-spatial analysis are:

1. Integration with mapping solutions: QlikView integrates not just with Google Maps, but also other leading mapping providers like Bing Maps, OpenStreetMaps, Mapbox, Esri ArcGIS, etc. This allows flexibility to choose the mapping technology that best suits your needs.

2. Associative geo-spatial analysis: QlikView‘s core value proposition is its associative engine, which automatically connects data across tables based on common fields. This enables combining geo-spatial and non-spatial data on the fly for deeper insights. For instance, you can analyze how sales performance correlates with store locations, consumer demographics, product categories, etc.

3. Robust map visualization options: QlikView provides several configuration options to design effective map visuals. You can choose between markers, heat maps, polygons, routes, custom icons, etc. to represent the spatial variables. The properties of each layer can be dynamically tied to measures and dimensions for maximum flexibility.

4. Geo-analytics and calculations: In addition to plotting data on maps, QlikView also supports geo-spatial calculations and advanced analytics. For instance, you can calculate distance and drive times between locations, identify nearest points of interest, cluster data points based on spatial density, etc.

5. Responsive design and interactivity: QlikView map visuals are highly responsive and re-draw quickly as you apply filters, drill-downs and other interactions. The ability to zoom, pan, select and hover over the map makes the analysis intuitive and engaging.

By leveraging these capabilities, analysts can create comprehensive geo-spatial applications in QlikView spanning a wide range of use cases such as:

• Site selection and territory management
• Demand forecasting and inventory optimization
• Fraud detection and risk monitoring
• Geo-marketing and customer profiling
• Route optimization and logistics planning

Configuring Google Maps in QlikView

Coming to the hands-on part, let‘s walk through the steps to configure Google Maps in your QlikView application. While the previous section covered the high-level steps, here are some additional details worth noting.

Setting up the Google Maps API Key

To use Google Maps in QlikView, you first need to set up an API key in the Google Cloud Console. Here‘s how:

1. Go to the Google Cloud Console (https://console.cloud.google.com/)
2. Create a new project or select an existing one
3. Enable the Maps JavaScript API and the Places API
4. Set up a billing account for the project (Google provides $200 free credits to get started)
5. Create an API key and restrict it to the specific APIs and domains where it will be used

Note down this API key, as you will need to plug it into the QlikView script.

Configuring the QlikView Script

With the API key handy, the next step is to generate the necessary variables in the QlikView script editor. Some additional details on the key configuration settings:

• max_zoom_level: This sets the maximum zoom level allowed for the map. A higher value will allow more granular zooming but can also impact performance. A good range is between 10-15, depending on the spatial spread of your data.

• map_size_x and map_size_y: These variables define the default size of the map control in pixels. Choose a size that provides enough real estate for the map while fitting well into your dashboard layout.

• var_maptype: This variable sets the default map theme. Options include roadmap, satellite, terrain and hybrid. You can either hard-code the preferred theme or allow the user to toggle between them.

The rest of the script generates the dynamic variables needed to render the map based on the user interactions and data values. Once the script is saved and reloaded, QlikView is primed for mapping.

Designing the Map Chart

The final step is to create the actual map object in the QlikView chart. In addition to the core dimensions and expressions covered earlier, here are some extra configurations to consider:

• Custom markers: By default, QlikView uses circular markers to represent data points. However, you can use custom images or icons to make the markers more informative. For instance, you could use different product images for each store location. Simply upload the images to a web server and reference the URLs in the marker settings.

• Info windows: Info windows are pop-ups that appear when the user clicks on a marker. You can customize the content of the info window to display additional details about the location, such as address, contact information, performance KPIs, etc.

• Layers: The map layers settings allow you to overlay additional information on the base map. For instance, you can add a layer of polygons to represent sales territories, a layer of routes to depict supply chain flows, a heat map layer to show customer density, etc.

• Calculated dimensions: QlikView‘s calculated dimensions enable you to modify the spatial data points on the fly based on user selections and filters. For instance, you could create a calculated dimension to show only the top N locations by sales or the locations that fall within a certain radius of the user‘s input address.

By tweaking these settings, you can design highly customized map charts that are both visually appealing and analytically powerful.

Geo-Spatial Analytics Trends to Watch Out For

As the volume and variety of location data continues to grow, the future of geo-spatial analytics looks very exciting. Here are some key trends that are shaping this space:

1. 3D Maps: The ability to render maps in three dimensions adds an extra layer of visualization to geo-spatial data. 3D maps are especially useful for analyzing variables that have a vertical component, such as elevation, building heights, atmospheric measurements, etc. Tools like Mapbox and ArcGIS already provide support for 3D mapping, and we can expect more BI platforms to follow suit.

2. Augmented Reality (AR) Maps: AR takes mapping to the next level by overlaying digital information on the user‘s real-world view. Imagine being able to point your smartphone camera at a street and seeing data on the businesses, landmarks and events in the vicinity. While AR is still in its early stages, it opens up fascinating possibilities for blending the physical and digital worlds.

3. Real-time Mapping: With the proliferation of real-time data streams from sensors, devices and social media, static maps are giving way to dynamic, self-updating maps. Real-time mapping enables monitoring of spatial phenomena as they unfold, such as traffic conditions, weather patterns, asset locations, etc. The challenge lies in ingesting and visualizing the data with minimal latency.

4. AI-powered Geo-Analytics: Artificial intelligence and machine learning are transforming every aspect of analytics, and geo-spatial is no exception. AI can enable smart features such as spatial clustering, anomaly detection, location-based forecasting, route optimization and more. By combining the predictive power of AI with the intuitive interface of maps, analysts can uncover actionable insights faster.

QlikView, with its open and extensible architecture, is well-poised to capitalize on these trends. As the platform continues to evolve, we can expect tighter integration with emerging mapping technologies and AI-powered geo-analytics.

Geo-spatial Dashboard Example

To tie it all together, let‘s look at an example of a geo-spatial dashboard built using QlikView and Google Maps. The dashboard is for a fictitious retail chain called "ShopSmart" with stores across the United States.

The dashboard consists of the following key elements:

1. A Google Map view of the store locations color-coded by performance quartile
2. A heat map overlay showing customer density by zip code
3. A side panel with filters for region, store format and date range
4. KPI cards showing total sales, average sales per store and sales growth
5. A bar chart of the top and bottom 5 performing stores
6. A scatter plot of sales vs. store size, color-coded by region

The map provides an instant snapshot of the geographic distribution and performance of the stores. Users can click on individual markers to drill down and view store-level details.

By combining the map with other charts and filters, analysts can perform a variety of tasks, such as:

• Identifying regional trends and outliers in performance
• Analyzing the impact of store size and format on sales
• Comparing store performance against customer density
• Forecasting demand based on historical sales patterns
• Planning store openings, closings and renovations

This example illustrates how geo-spatial visualizations can serve as the centerpiece of a comprehensive business intelligence dashboard, providing a unified view of location-based insights.

Conclusion

In a world where location is becoming an increasingly critical dimension of data, geo-spatial analysis has evolved from a nice-to-have to a must-have capability for organizations. By leveraging tools like QlikView and Google Maps, analysts can weave compelling data stories that combine the power of geography with the rigor of statistics.

As you embark on your geo-spatial analytics journey with QlikView, keep in mind the best practices covered in this guide – from choosing the right map type and zoom level to layering on additional dimensions and context. Don‘t be afraid to experiment with different configurations and designs to find what works best for your use case.

Most importantly, always keep the end user in mind. The ultimate goal of any geo-spatial dashboard is to enable data-driven decision making and action. By creating intuitive, interactive and insightful map-based visualizations, you can help your stakeholders navigate the complex world of location data with confidence and clarity.

Happy Mapping!