A Platform for the Analysis of Qualitative and Quantitative Data about the Built Environment and its Users

There are many scenarios in which it is necessary to collect data from multiple sources in order to evaluate a system, including the collection of both quantitative data - from sensors and smart devices - and qualitative data - such as observations and interview results. However, there are currently very few systems that enable both of these data types to be combined in such a way that they can be analysed side-by-side. This paper describes an end-to-end system for the collection, analysis, storage and visualisation of qualitative and quantitative data, developed using the e-Science Central cloud analytics platform. We describe the experience of developing the system, based on a case study that involved collecting data about the built environment and its users. In this case study, data is collected from older adults living in residential care. Sensors were placed throughout the care home and smart devices were issued to the residents. This sensor data is uploaded to the analytics platform and the processed results are stored in a data warehouse, where it is integrated with qualitative data collected by healthcare and architecture researchers. Visualisations are also presented which were intended to allow the data to be explored and for potential correlations between the quantitative and qualitative data to be investigated

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil

A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.This work has been supported by the Cost Action TU1306, called CYBERPARKS: Fostering knowledge about the relationship between Information and Communication Technologies and Public Spaces supported by strategies to improve their use and attractiveness, the Spanish Ministry of Economy and Competitiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and the TARSIUS project (ref. TIN2015-71564-C4-4-R), and the Basque Country Department of Education under the BLUE project (ref. PI-2016-0010). The authors would also like to thank the staff of UbiSive s.r.l. for the support in developing the application

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Monitoring of gas emissions at landfill sites using autonomous gas sensors

Executive Summary This report details the work carried out during the Smart Plant project (2005-AIC-MS-43-M4). As part of this research, an autonomous platform for monitoring greenhouse gases (methane (CH4), carbon dioxide (CO2)) has been developed, prototyped and field validated. The modular design employed means that the platform can be readily adapted for a variety of applications involving these and other target gases such as hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) and the authors are in the process of completing several short demonstrator projects to illustrate the potential of the platform for some of these applications. The field validation for the greenhouse gas monitoring platform was carried out at two landfill sites in Ireland. The unit was used to monitor the concentration of CO2 and CH4 gas at perimeter borehole wells. The final prototype was deployed for over 4 months and successfully extracted samples from the assigned perimeter borehole well headspace, measured them and sent the data to a database via a global system for mobile (GSM) communications. The data were represented via an updating graph in a web interface. Sampling was carried out twice per day, giving a 60-fold increase on current monitoring procedures which provide one gas concentration measurement per month. From additional work described in this report, a number of conclusions were drawn regarding lateral landfill gas migration on a landfill site and the management of this migration to the site’s perimeter. To provide frequent, reliable monitoring of landfill gas migration to perimeter borehole wells, the unit needs to: • Be fully autonomous; • Be capable of extracting a gas sample from a borehole well independently of personnel; • Be able to relay the data in near real time to a base station; and • Have sensors with a range capable of adequately monitoring gas events accurately at all times. The authors believe that a unit capable of such monitoring has been developed and validated. This unit provides a powerful tool for effective management of landfill site gases. The effectiveness of this unit has been recognised by the site management team at the long-term deployment trial site, and the data gathered have been used to improve the day-to-day operations and gas management system on-site. The authors make the following recommendations: 1. The dynamics of the landfill gas management system cannot be captured by taking measurements once per month; thus, a minimum sampling rate of once per day is advised. 2. The sampling protocol should be changed: (i) Borehole well samples should not be taken from the top of the well but should be extracted at a depth within the headspace (0.5–1.0 m). The measurement depth will be dependent on the water table and headspace depth within the borehole well. (ii) The sampling time should be increased to 3 min to obtain a steady-state measurement from the headspace and to take a representative sample; and (iii) For continuous monitoring on-site, the extracted sample should be recycled back into the borehole well. However, for compliance monitoring, the sample should not be returned to the borehole well. 3. Devices should be placed at all borehole wells so the balance on the site can be maintained through the gas management system and extraction issues can be quickly recognised and addressed before there are events of high gas migration to the perimeter. 4. A pilot study should be carried out by the EPA using 10 of these autonomous devices over three to five sites to show the need and value for this type of sampling on Irish landfill sites