{"id":11881,"date":"2018-01-21T16:31:56","date_gmt":"2018-01-21T15:31:56","guid":{"rendered":"https:\/\/www.intellias.com\/?p=11881"},"modified":"2025-07-09T16:36:58","modified_gmt":"2025-07-09T13:36:58","slug":"spatial-data-visualization","status":"publish","type":"post","link":"https:\/\/intellias.com\/spatial-data-visualization\/","title":{"rendered":"Spatial Data Visualization for End Customers Convenience"},"content":{"rendered":"

Business challenge<\/h2>\n

A technology company that provides innovative\u00a0data services and mapping solutions<\/a>, our client is listed among the world\u2019s top 10 LBS providers. Their product portfolio comprises multilayer HD maps, mapping data visualization<\/span>,\u00a0<\/span>integrated in-dash navigation systems for global car brands, location services for enterprises and municipalities, and a collaborative ecosystem powered by real-time location intelligence.<\/p>\n

By digitizing reality, our client strives to make a safer and more comfortable future for us all \u2014 drivers navigating unfamiliar backroads, tourists trying to decipher the Paris metro map, or traffic incident managers executing an accident response plan using mapping data visualization<\/span>.<\/p>\n

\"Spatial<\/h3>\n

Nowadays, the term location intelligence is used to describe geographically related data as the basis for insightful decision-making and is applicable to virtually any business vertical. Our client provides an advanced mapping data visualization <\/span> platform that exposes location intelligence\u00a0including maps, geocoding, traffic, routing, POIs, and interactive map visualization<\/span>. The company is constantly improving their platform as a whole and its constituent services.<\/p>\n

Since 2015, Intellias has been involved in this improvement process on a number of workstreams including traffic feeds, public transit data, rendering, indoor maps, and GIS 3d modeling <\/span>. Our client wanted to rebuild their legacy visualization component. Among their goals were boosting its performance, deployability, and extensibility and packing it with new features.<\/p>\n

Technology solution<\/h2>\n

Since March 2018, Intellias has been contributing to the development of an API toolkit for building complex geo data visualization of location-based datasets on top of maps to facilitate geospatial analysis.<\/p>\n

Initially, Intellias set up an engineering team that extended our client\u2019s Berlin-based development team. As the Berlin team was being migrated to other business units, Intellias engineers progressively acquired project knowledge about map visualization<\/span>\u00a0from them. Ultimately, it took us about two months to grow from an extension<\/a> to a dedicated development team that followed the agile Kanban methodology.<\/p>\n

The first difficulties we coped with related to the client\u2019s IT infrastructure for GIS 3D modeling <\/span>not being built on mainstream technologies like Docker for the build environment or Kubernetes for container orchestration. Although their infrastructure was based on Amazon Web Services, Puppet and some of their proprietary tools (including their own configuration application) left little room for quick deployment optimization. Intellias engineers spent a good amount of time investigating dependencies and how these tools worked together. As a result, our team improved the time-to-market value for the spatial data visualization services<\/span> by optimizing deployment and production validation processes from 1.5 to 2 hours to merely 25 to 40 minutes on average.<\/p>\n

Tackling ongoing issues of our client\u2019s existing customers is another responsibility of the Intellias team. One of our client\u2019s customers, a tech company offering geo-powered data services for advertisers, had problems with our client\u2019s storage performance. Based on AWS Redshift, the data warehouse<\/a> worked great with infrequent large queries for mapping visualization,<\/span>\u00a0but lagged when executing multiple small queries. Our task was to come up with a proof of concept comparing the throughput of AWS Redshift and AWS RDS PostgreSQL for multiple small queries. Our conclusions made our client rethink their product investment strategy. It became clear that their product audience could be extended from big businesses to small companies and individual consumers.<\/p>\n

To implement some specific features, the Intellias mapping visualization <\/span> team needed to look for non-standard technical solutions. In particular, our frontend engineers implemented a mechanism for visualizing hexagonal heatmaps. For this, we composed a query for deriving a group of points on a plane limited by the nearest hexagon in a grid. The query was written using an internal domain-specific language (DSL) that had certain limitations \u2014 for example, it was Turing incomplete. Our engineers applied a few unusual techniques to overcome the DSL limitations.<\/p>\n

Our engineers are also dealing with Hibert curve optimization. This consists of converting a two-dimensional coordinate system into a one-dimensional system. The approach minimizes the computational effort thanks to preliminary data processing.<\/p>\n

Business impact<\/h2>\n

The partnership with Intellias has enabled our client to spot new business opportunities for their location-related map visualization<\/span> service. As a result, our client is considering a few investment choices. Intellias team helped our client to tap on 3D visualization GIS services<\/span>.<\/span><\/p>\n

They want to provide better integration options, so their 3D visualization GIS\u00a0<\/span>service is reusable by other existing and future products. In addition, our client started rebuilding their mapping visualization <\/span> data sets so that small businesses and individual consumers can use them efficiently.<\/p>\n

The solution that Intellias is helping to develop allows:<\/h4>\n
    \n
  • Securely uploading, storing, and visualizing location-related customer data over fast web maps<\/li>\n
  • Transforming, aggregating, and filtering customer data on the server side (for large volumes) or on the client side (for small volumes)<\/li>\n
  • Gaining meaningful insights through\u00a03D geospatial visualization<\/span> data (static and dynamic markers, heat maps, raster maps, and more)<\/li>\n
  • Extracting latitude and longitude values from street names and postal codes due to extended geocoding capabilities<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    We\u2019re helping a global LBS company to improve spatial data visualization for advertisers, transportation and mobility service providers<\/p>\n","protected":false},"author":6,"featured_media":49797,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5],"tags":[37,75,908,910,458],"class_list":["post-11881","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-study","tag-cloud-devops","tag-data-analytics","tag-intelligent-transport","tag-mobility","tag-transportation","technologies-angular-4","technologies-node-js","technologies-postgresql","technologies-preact","technologies-python","technologies-redshift","technologies-webgl"],"acf":[],"yoast_head":"\nSpatial Data Visualization for End Customers Convenience | Intellias<\/title>\n<meta name=\"description\" content=\"Learn how Intellias helped a global LBS company to improve 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