Challenge

Constructive Geographic Business Intelligence is crucial to the success of any product but can be prohibitively expensive to acquire or produce.

Solution

With GeoMarkeTool, OODA proposes an approach that does away with conventional barriers of entry. Backed by strong design principles centered on ease of use and clarity, people of all backgrounds can use GeoMarkeTool to gain access to valuable insight and align their business to be more focused and more efficient.

The web app allows the user to visualise geolocalized statistical data on a map at the district level with the possibility of adding data on demand.

Challenge

The impact of business metrics tends to be substantial compared to their cost but serious investment is still required to develop and maintain the necessary skill sets in-house. With these in place, the challenge of designing the right visualisation to effectively pass on the intel to unbriefed team members, still remains.

Solution

OODA answers the need for on-demand analysts and provides Finecomb as a means to custom-visualise its findings. Finecomb is a web application with personalized dashboards for metrics and geo-referenced data visualisation. It provides simple but powerful options to visualize and customize metrics. With a design focus on adaptability, Finecomb can be adjusted to accommodate any desired metric or database.

Finecomb was developed in collaboration with Spearhead Games and is used to provide game analytics for Arena Cyber Evolution (ACE). It has also been used by the Stationnement de Montréal for a study on parking availability prediction.

Challenge

While discussing the day-to-day of his job, a portfolio manager specialized in commodity investments, confided in us that he had to wade through more reports and articles than he could ever read in a single day. Being forced to pick some and discard many others, he would invariably be left with the feeling that he’d missed something important.

Indeed, most commodity trading tools focus on quantitative measures (quotes, interest rates, historical data). With BullBear, OODA aims to better synthesize qualitative information into the decision making process. The goal is to develop an app to process thousands of news articles, blogs or newsletters a day, and aggregate the main takeaway concerning each commodity mentioned.

Solution

OODA developed BullBear to empower commodity traders to quickly gauge the prevailing market mood. It successfully aggregates and analyzes commodity news documents from the web and extracts information about a commodity’s price shift. It also produces a real-time index indicating the direction and magnitude of price variation. During its four years of service, the index produced by BullBear has been shown to be a predictive factor of daily volatility of the commodity market.

Challenge

Research has shown that scanning twitter for keywords evoking mood can provide a predictive indicator for the direction of the stock market. However, translating a Twitter stream into a usable index is far from straightforward.

Solution

OODA partnered with Toronto-based Qmetrica Quantitative Finance Inc., in an experiment called Marketsense. Our goal was to investigate natural language processing techniques and perform a multidimensional sentiment analysis of the Twitter stream. The experiment drew to a close in the last quarter of 2014.

Challenge

The Plateau-Mont-Royal borough encompasses concentrated residential areas, a slew of thriving businesses and some of the narrowest streets in Montreal. In order to enact efficient parking regulations, readings of geo-referenced license plates are collected and analyzed for occupation patterns.

However, the process of parsing and analysis is lengthy and complex.

Solution

OODA was called in to develop an analysis suite for the automatic study of license plate readings and help streamline the performance measurements of applied parking regulation. The result is a web application named AutoTri that enables city officials to be much more responsive to the needs of its citizens by reducing their workload from days to minutes.

AutoTri was featured at Vision Geomatique 2014

Challenge

As part of its smart city initiative, as well as to better satisfy citizens, the limited partnership business entity, Stationnement de Montreal wishes to explore the predictive potential of its data. The goal is to predict real-time parking availability on any given street section. OODA was called on to study the feasibility of this ambition.

Solution

OODA experimented with 6 predictive analytics models to evaluate street occupancy. The results show it is possible to forecast parking availability with a surprisingly high degree of confidence.

Challenge

Through various discussions with city officials over the years, we recognized a recurring desire to visualize the evolution of geo positioned data in time. An understandable need, considering the amount of spreadsheets and line charts an analyst usually has to evaluate.

It’s intuitive that, the simplest way to understand and assimilate geo positioned data is on a map. Adding a time dimension to the visualization entails increased technical challenges but the results are equally rewarding.

Solution

We developed CityFlow to dynamically visualize traffic flow at intersections, using open data for pedestrian, cyclist and vehicle counts. The simplicity of the interface allows users to navigate intuitively and lets the data speak for itself.

Challenge

The AMT regularly surveys the Montreal area commuters’ travel patterns and publishes the results. The data is useful to the general public and especially to businesses looking to extract intelligence, but is impractical to navigate in its distributed form.

Solution

OODA’s goal was to present the data in an intuitive way for anyone willing to extract meaning. The result is an interactive chart application with a straightforward delivery of commuter habits and trends. Through custom filters, it is possible to isolate a specific mode of transport, time of day and reason for travel.

Challenge

To support their research related to wide area surveillance in Canada’s three oceans, the Maritime Information Support (MIS) group in DRDC Atlantic needed an infrastructure that processes and stores daily in real-time over 25 GB of data from various maritime transportation data sources.

Solution

Installed at DRDC-Atlantic in May 2013, this infrastructure developed by OODA has the largest maritime database (actual size of 9 TB with 80 TB capacity) among all DRDCs and allowed the MIS group to shift from application and information silos paradigm to an integrated framework.

Challenge

An increasing number of sensor and information sources are being employed to provide surveillance coverage of very large maritime areas. These systems have regions of overlapping coverage, which result in multiple tracks being reported for a single maritime platform. The advantage gained by having multiple sources can soon be outweighed by the ambiguity resulting from having multiple tracks for the same platform.

Solution

OODA developed for DRDC-Ottawa a High-Frequency Surface Wave Radar (HFSWR) track-to-track fusion facility in which maritime vessels detected by coastal radars are correlated with another type of information, provided by an Automatic Identification System (AIS). In an operational context, the correlator first attempts to identify the same vessel reported by different sensors based on track kinematics and geospatial information, and then combines both tracks into a single one.

The results are demonstrated in Google Earth, and show that the combination of information from two sensors provides a more detailed positional history of a vessel and reduces the clutter in the display area of the operator monitoring software.

Challenge

Potential issues can arise when units are engaged in collaborative joint or coalition force Joint Fires operations: trust, language, difference in training, etc. To gain a better understanding at these issues, DRDC-Ottawa performed a long term research (Joint Fire Support) involving, among others, the evaluation of the performance of operators during human factor experiments. These experiments required the analysis of capture data such as screen snapshot, mouse and keyboard capture and operator communications collected during the joint human factor experiments.

Solution

OODA developed and implemented a data capture analysis toolkit to support DRDC scientists in the analysis of large quantities of operator analysis data. OODA’s toolkit includes many technologies such as optical character recognition for extracting action information from screenshots, social network analysis tools and graphical displays for operator communication analysis, and the ability to associate data with Department of Defence Architecture Framework Operational Activity Models.