Data and Geometry; Model Building at Calthorpe Analytics

This report documents my Summer 2016 internship with Calthorpe Analytics, a Berkeley CA based urban planning firm. Calthorpe Analytics specializes in scenario development for planning, modeling, and plan evaluation for government and municipal clients. The primary responsibility of my internship was model development and refinement using advanced spatial analytics working in the Python programming language. The internship was extremely successful: it gave me a great opportunity to strengthen my open source GIS skillset, deepen my understanding of data science, and vastly improve my geospatial programming skillset. It also gave me a chance to apply advanced geospatial modeling and spatial statistics in practice. The firm provided a great working environment and a very supportive culture in which to learn and test new ideas and techniques. The following paper will expand on the work of Calthorpe Analytics, their culture and organization, my contributions to their workflow, and reflect on the personal and professional impact of the internship

GEO-VISUALISATION AND VISUAL ANALYTICS FOR SMART CITIES: A SURVEY

Geo-Visualisation (GV) and Visual Analytics (VA) of geo-spatial data have become a focus of interest for research, industries, government and other organisations for improving the mobility, energy efficiency, waste management and public administration of a smart city. The geo-spatial data requirements, increasing volumes, varying formats and quality standards, present challenges in managing, storing, visualising and analysing the data. A survey covering GV and VA of the geo-spatial data collected from a smart city helps to portray the potential of such techniques, which is still required. Therefore, this survey presents GV and VA techniques for the geo-spatial urban data represented in terms of location, multi-dimensions including time, and several other attributes. Further, the current study provides a comprehensive review of the existing literature related to GV and VA from cities, highlighting the important open white spots for the cities’ geo-spatial data handling in term of visualisation and analytics. This will aid to get a better insight into the urban system and enable sustainable development of the future cities by improving human interaction with the geo-spatial data

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

Usability of Open Data for Smart City Applications – Evaluation of Data, Development of Application and Creation of Visual Dashboards

Today different sources of information on urban areas are becoming openly available at various spatial and temporal resolutions and extents. They are crucial for driving towards “Smart Cities”. Many smart cityrelevant applications, to understand and predict certain phenomena such as mobility, air quality, etc., depend on large amounts of readily available good quality data. Many datasets related to such topics are already publicly available. However, the appropriate use of these datasets must be ensured by checking the quality of data in a systematic way. Under quality of data, one not only evaluate the number of missing or false data points but also determine data characteristics such as resolution, frequency and ease of use, etc. Therefore, the objectives of this paper are to evaluate the open data available in different portals (80 in total) with special consideration to these factors and to evaluate their usability in some of the smart city applications. In this regard, an extensive literature review is carried out. We observed that especially official government data portal often lack these qualities. This could have occurred due to the lack of concrete examples of how cities and citizens can profit from the applications created with the appropriate kind of data. Some civil servants might have experienced some levels of mistrust regarding the abstract ideas of ‘Smart City’ and ‘Open Data’. We therefore illustrate three possible applications, e.g. (a) use of high-resolution low- cost sensor data around Europe (b) GPS trajectories of a large number of taxis monitored inside the city and (c) land-use and accesability data from voluntered geographic information. In this regard, other open source spatial data portals (such as Open Street Map APIs) and open source software such as python and relevant libraries are also utilized. For each application, we elaborate the data characteristics and the detailed methodological steps (e.g., analytics methods) as well as communicate the results with an easy to operate dashboard having strong visualisation and analytical aids (maps, graphs, statistical summary, etc.). The dashboards help to understand the significance of open data and to support decision makers in creatingservices for the citizens with the context of “Smart City”. Finally, we conclude with the limitation and further recommendations to the city officials regarding their role of shaping the future of (smart) cities with the right open data policy

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin