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MinneAnalytics Mondays: Finding Purposefully Hidden Sites with GPUs and ML

Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data?

OmniSci’s GPU database technology lets us combine several factors into a suitability model considering roads and their relationships to terrain. We leveraged an amazing new machine learning product from Planet - a monthly road change dataset at 5 meter resolution. We combined this with absolute elevation, percent slope and topographic position. Since there are less than 20 known sites, we elected to use a “human in the loop” process to empower analysts to assess the parameters of known sites semi-manually, and then to search for similar sites across the full country. This allowed us to discover hundreds of potential new sites, which CNS plans to further explore and then monitor.
Recorded Jul 16 2020 33 mins
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Presented by
Mike Flaxman, Spatial Data Science Lead, OmniSci & Adam Edelam, Federal Solutions, OmniSci
Presentation preview: MinneAnalytics Mondays: Finding Purposefully Hidden Sites with GPUs and ML

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    Dr. Mike Flaxman, Spatial Data Science Lead, OmniSci
    5G Is on its way to consumer markets in the coming years. 5G network infrastructure is expected to completely revolutionize network connectivity. With proper 5G network planning and optimization, telecommunications companies will be able to deliver better customer experiences, solve complex problems, and move their business forward. 5G and big data go hand-in-hand. Experts predict 5G data usage could increase by 10-14 times current figures. This astronomical influx of 5G data creates an incredible opportunity for telco companies to explore real-time 5G insights and leverage 5G data analytics for 5G network optimization that gives your customers fast and consistent network connectivity at all times.
  • Predicting Census Undercount Areas with Massive GPS Movement Pattern Data Oct 14 2020 10:00 pm UTC 30 mins
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    Undercounting within the US National Census has always been a significant issue for specific populations, such as those with complex family structures. With the rise of Covid-19 and a shortened counting period, this year’s census is probably the most challenging to date. How can data science and new datasets help?

    We found that modern machine learning techniques can provide the basis for much-improved census undercount prediction. We also found that the addition of GPS data and point of interest data increased model accuracy and provided further insight into behavioral factors beyond demographics which significantly affect undercount.
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    Human mobility patterns have been studied for millennia and are of obvious interest to transportation infrastructure and retail planners among others. It is only in the last few years that consumer GPS and cell phones have allowed observations of large samples over wide areas and long periods of time. Yet the mechanisms used by these services vary widely and should be expect by default to contain demographic sampling biases. We have conflated these GPS-derived spatial event stream data sources with traditional census block group data in order to quantify and calibrate them. We have used these adjustments to produce the first known map of US populations at an hourly basis before and during the COVID-19 epidemic. In order to protect individual privacy, we have deployed an explicit aggregation system using variable-sized hexagonal bins. Nonetheless, our maps represent much higher resolution data than conventionally available, ranging from 50m in dense urban areas to 1km in non density rural areas. Because sampling along roads is much higher than elsewhere, we are able to provide traffic estimates at high spatial precision nationally. In addition to overall population density, we have used overnight device dwell times in residential areas to infer the hourly demographic characteristics of areas based on device trajectories.
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  • MinneAnalytics Mondays: Finding Purposefully Hidden Sites with GPUs and ML Recorded: Jul 16 2020 33 mins
    Mike Flaxman, Spatial Data Science Lead, OmniSci & Adam Edelam, Federal Solutions, OmniSci
    Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data?

    OmniSci’s GPU database technology lets us combine several factors into a suitability model considering roads and their relationships to terrain. We leveraged an amazing new machine learning product from Planet - a monthly road change dataset at 5 meter resolution. We combined this with absolute elevation, percent slope and topographic position. Since there are less than 20 known sites, we elected to use a “human in the loop” process to empower analysts to assess the parameters of known sites semi-manually, and then to search for similar sites across the full country. This allowed us to discover hundreds of potential new sites, which CNS plans to further explore and then monitor.
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  • Title: MinneAnalytics Mondays: Finding Purposefully Hidden Sites with GPUs and ML
  • Live at: Jul 16 2020 8:00 pm
  • Presented by: Mike Flaxman, Spatial Data Science Lead, OmniSci & Adam Edelam, Federal Solutions, OmniSci
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