This is a small, very small update from one of my previous posts. However, the original post was shared on another blogging platform. In the near future, I will migrate these old posts into digital emerge… of course, after updating the articles, correcting them or augmenting them, when possible.

Since the original post was shared in 2018, we have published a very interesting follow-up article. In the article, we explore the use of different data sets as proxies to improve human mobility predictions in Oslo. Check out the article’s abstract:

Assessing alternative population size proxies in a wastewater catchment area using mobile device data

Modeling and prediction of a city’s (Oslo, Norway) daily dynamic population using mobile device-based population activity data and three low cost markers is presented for the first time. Such data is useful for wastewater-based epidemiology (WBE), which is an approach used to estimate the population level use of licit and illicit drugs, new psychoactive substances, human exposure to a wide range of pollutants, such as pesticides or phthalates, as well as the release of endogenous substances such as oxidative stress and allergen biomarkers. Comparing WBE results between cities often requires normalization to population size, and inaccuracy in the measured population can introduce high levels of uncertainty. In this study mobile phone data from 8-weeks in 2016 was used to train three linear models based on drinking water production, electricity consumption and online measurements of ammonium in wastewater. The ammonium model showed the best correlation with an R-squared of 0.88 while drinking water production and electricity consumption showed more discrepancies. The three models were then re-evaluated against 5-week of mobile phone data from 2017 showing mean absolute errors <10%. The ammonium-based estimated mean annual population for Oslo in 2017 was 645 000 inhabitants, 4% higher than the “de jure” population reported by the wastewater treatment plant. Due to changing conditions and seasonality, drinking water production underestimated the population by 27% and electricity consumption overestimated the population by 59%. Therefore, the results of this work showed that the ammonium mass loads can be used as an anthropogenic proxy to monitor and correct the fluctuations in population for a specific catchment area. Furthermore, this approach uses a simple, yet reliable indicator for population size that can be used also in other areas of research.

You can find the whole article on:

IoT sensors, big data and advanced analytics to ensure optimal growth

Movement is a fundamental part of our lives, not just when we’re enjoying the countryside with our family or jogging in the nearby park. For many of us, the practical part of our lives relies much on the services offered by our city.  Many important decisions in our modern society and business life could benefit from the insight into the society’s mobility. 

Traditional methods to solve mobility challenges provide scarce data both geographically and from the perspective of time. Often decision-makers rely on very expensive manual counting or field surveys as data sources to optimize the construction of new infrastructure such as roads and parks within a city.

In this first from a series of posts, we will write about the use of IoT sensors, Big Data and Advanced Analytics, and how they can be used to improve mobility, economic growth and even innovation in a city!

The internet of things (IoT)

Even though the IoT has been much of a buzz word in the past few years, it really is much more than that. The IoT is already transforming our everyday business practices, creating new windows of opportunity for innovation and optimization within different industries. According to IHS, the number of connected IoT devices globally will jump an average of 12% every year. They estimated that during 2017 there were nearly 27 billion IoT devices, while it is expected that there will be 125 billion by 2030. The number of devices that are already operating are generating a massive amount of data, and worldwide data transmissions are expected to increase from 20% annually to 50% per year during the next 15 years. According to the statistics from NKOM, in Norway the data that IoT devices are generating is already increasing around 50% every year. Norway is a country that has moved, and continues to move, into digitalization at a very fast pace. When a country moves into a digital enabled industry and economy in the way that Norway is doing it, new sources start producing data with high volume, velocity, and variety… also known as Big Data.

Big Data

The first documented use of the term «Big Data» appeared in a 1997 paper by scientists at NASA, describing the problem of visualizing very large data sets. Ever since the introduction of the Big Data term in 1997, a few definitions for it have arisen, with the «Triple V» definition being widely accepted (Volume, Velocity, Variety). Nowadays we can use the advances in Big Data technologies to analyze datasets composed of billions of rows (and Terabytes!) in real time. See for example Figure 1. where we show the paths followed by ships in Norway, in Acando we have analyzed a dataset with more than a billion data points with the use of cloud-based infrastructure.

Figure 1. Visualization of AIS data. The dataset represents the location and navigation of ships along the Norwegian coastline. The dataset is composed of over a billion data points.

Big Data has been a source of innovation, and with the right use of this technology by policymakers and business leaders it can continue improving our everyday lives. Making use of Big Data technologies has the potential to accelerate economic growth and sustainable development.

In the present, and with the help of Big Data, society can step up in its focus on evidence-based policy making and monitoring of development progress. It is therefore key to have the expertise of combining different data sources with advanced analytics. With these capabilities, one can tap into the opportunity of creating value for decision makers and society.

Society’s predominant engine of innovation

Cities have been known to be the predominant engine and driver for innovation and wealth creation in a society. On the other hand, they are also society’s main source of crime, pollution and health problems. In the present, the rapid urbanization and city growth raises an important challenge: How do we promote growth and urbanization while keeping crime, pollution, and disease as low as possible?

It has been shown (see Growth, innovation, scaling, and the pace of life in cities) that many diverse properties of cities, from patent production to personal wealth, are power-law functions of population size and correlate to the degree of mobility within the city and its surroundings. Quantities reflecting wealth creation and innovation have an exponent β > 1 (increasing returns). This means that if city A produces X number of patents, city B with double the population size produces more than double the number of patents in the same period, i.e. The larger the city, the more innovation and patents it can create! Quantities reflecting the needs for infrastructure have exponents β < 1 (economies of scale). This effectively means that city B with double the population of city A, does not require double the investment in infrastructure, i.e., it is economically preferable to have larger cities.

One consequence of this power-law functions is that, as population grows, major innovation cycles must be generated at a continually accelerating rate to sustain growth and avoid stagnation or collapse.

Using IoT, Big Data and Advanced Analytics to enable urban growth through smart mobility

Movement, the way a society flows within a city, is a fundamental part of life. The degree of progress of a society correlates directly to the level of mobility as well as the means for it. A way in which a city can grow and be able to get the benefits mentioned above, while reducing the negative impacts of pollution and health problems, is to ensure that populations have the best means to commute to and from work, transport to recreational places, flow through the city’s mobility infrastructure in a frictionless way.

How do we improve in traffic management?, how do we plan for railway constructions or expansions?, where should we place a new shopping center?, when should retailers have marketing campaigns to better attract their segments of interest?, how does a store or distribution center need to be placed in order to have the best reach of customers?, how do diseases spread?, how should a stadium or concert venue be operated during a massive event such as a concert?… We can better answer these questions by using IoT sensors, Big Data and Advanced Analytics.

In a series of upcoming blog posts, we will present ways in which different industry verticals can benefit from the knowledge on how we, as a society, move and modify our mobility patterns depending on several factors. We will also introduce the reader to the variety of data sources that can help us understand human mobility. Among the verticals that can greatly benefit from this knowledge are:

  • Public transportation
  • Health
  • Retail
  • Smart city development
  • Maritime transport

Until then, happy commute!

About the writer

Arturo is a technologist born in Veracruz, Mexico and has been living in Norway since 2010. He completed his BSc and MSc in Mexico and his PhD in Trondheim, Norway. He is taking the last semester of his Executive MBA at BI in Oslo, to be concluded in September 2020.

During his experience as a scientific researcher, which started with the publication of a couple of papers in international journals after his BSc thesis, he has participated in several research projects. The results of these projects can be found in the 6 research papers shared below. The topics of these projects vary from theoretical physics, mathematics, statistics, and applications of Big Data technologies to understand human behavior. Part of Arturo’s training as an academic includes the use of different programming languages and scientific tools such as Mathematica, Matlab, C++, being Python one of his most common go-to tools.

After concluding his PhD work at NTNU, Arturo made a transition into the private sector in 2015. He started leading the development of the Mobility Analytics service in the business division of Telenor Norway. Arturo led the development of this service, from strategic aspects and all the way to the technical aspects of the development. Not only did Arturo work in tasks such as developing Go To Market strategies but also with hands-on development of GIS data visualizations and development of the code-base to power the Mobility Analytics Service.

The extensive experience as a leader and communicator in the scientific and private sector fields has made Arturo a firm believer that it is not often that leaders and executives make decisions based on hard, cold numbers, but that it is necessary to communicate the results from the analytic work via great story-telling and understanding of the business mechanisms in which value can be created. This realization made Arturo take the decision to start his EMBA program in 2019.

During his EMBA studies with a specialization in managing and developing digital enterprises, and which include a leadership program, Arturo has expanded his business knowledge to include fields such as digitalization strategies, innovation frameworks (such as design thinking lean and agile), entrepreneurship, HR management, and value creation and capture.

As his professional track shows, Arturo has a mind eager to always learn and apply his knowledge. He has taken several courses and certifications within Machine Learning and Artificial Intelligence and has virtually never stopped studying, even after concluding his doctoral degree.

Arturo enjoys mentoring and supervising and helping colleagues and people in general. He is always open to grab a coffee, a slice of pizza or a pint of beer, so… feel free to send him a message and connect with him, even if you haven’t met him in person.

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