We will move dynamic data filtering logic into the GPU side, using customized shader modules by deck.gl ( data-filter-rfc). To improve aggregation speed, we will also integrate with deck.gl’s GPU grid aggregation layer.ĬPU filtering causes lagging on large datasets, especially during time playback. To resolve rendering lags caused by expensive CPU computation during dynamic filtering, we will move some of the filtering logic to GPU, using deck.gl’s customized filtering shader module. In this milestone, we will focus on improving performance in two areas: dynamic filtering and grid aggregation. Most of it is because we are still processing large array of data in CPU. We see complaints about the app crashing, lags during filtering, aggregation and domain calculation. That’s not to say we are doing everything right. One of kepler.gl’s strong competitive edge is its ability to process and visualize a large amount of data, fast, inside the browser. Performance and Ubiquitous Performance Improvement We will add more documentation and framework API to help people use kepler.gl as a framework. To encourage external contribution, we also plan to add contribution guidelines and publish our roadmap. Finally, we plan to support data manipulation such as spatial joining and integrate location services for geocoding and routing.
We will also address many enhancements to the usability of layers, filters, and interactions. To further establish kepler.gl as an advanced geo-analytics tool, we will implement essential geo operations such as drawing, filtering/aggregation by geo-boundaries, add new layers including edge bundling and trip animation layer for advanced visualization. We will utilize GPU to accelerate filtering and aggregation, make it easy to save/share/embed kepler.gl and create kepler.gl plugins for tableau and jypyter notebook. Moving on to 2019, we want to push the development of kepler.gl along 4 main themes: Performance & Ubiquitous, Advancing geo-analytics, Improving Usability and Data Integration. We are excited to release it to the public so that everyone can contribute to kepler.gl and help us achieve our goals. This roadmap includes all the items we want to work on for 2019. There are many opportunities ahead, and we want to plan our 2019 roadmap based on all the feedback we got from people using kepler.gl. They also wish to integrate other data types into kepler.gl or it connects to different databases. For usability, people often compare kepler.gl to Carto, ArcGIS, they requested many useful features on top of the existing layer, filter and interaction options. Another universal requested function is the ability to save and share maps or embed an interactive map on a web page. Many mentioned UI lagging when handling large dataset. Many people also mentioned that the arc layer, 3d hexagon, and time animations are unique features that they love the most.Īmong the potential areas that we can improve on, performance came as the top issue. They are impressed by its ease of use, speed, and the beautiful visualizations.
#KEEPLAR PHOTOX SOFTWARE#
The majority of our users are data scientist/analysts, software engineers, GIS specialist, and academic researchers. People spent an average of 8 minutes on kepler.gl. It has 100k unique users to this day and used by engineers from Airbnb, Google sidewalk lab, UNICEF etc. Kepler.gl has attracted great interests following its launch 8 months ago. Allow loading base map tiles from a custom tile server.Allow connecting points based on sequence.Allow drawing on map to create paths and polygons.Contribution Guidelines for GitHub community.
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