Quantifying the spatio-temporal temperature dynamics of Greater London using thermal Earth observation

PhD ThesisUrban areas are highly sensitive to extreme events such as heatwaves. In order to understand how cities will respond to thermal stress it is critical to quantify not only their temporal temperature dynamics but also their spatial temperature variability. However, many cities lack weather station networks with a sufficient spatial distribution to characterise spatio-temporal intraurban temperature dynamics. One means by which spatially complete measurements of urban temperature may be derived is to employ satellite thermal Earth observed data. While some success has been achieved in understanding the temperature characteristics of cities using such data, relatively little work has been undertaken on establishing the use of long time-series Earth observed data as a supplement or alternative to screen-level air temperatures frequently utilised in urban climatological studies. In this thesis a software framework, centred around the use of a spatial database, is developed which can be used to gain an improved understanding of how satellite thermal Earth observed data can be used in the long timeseries analysis of urban temperature dynamics. The utility of the system is demonstrated by processing a 23 year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly United Kingdom (UK) Met Office weather station measurements for the Greater London area. London was selected as the region of interest as it is the UK’s only megacity, and has been shown to exhibit both a significant urban heat island and a severe increase in population mortality during previous heatwave events. The software framework was employed to conduct two inter-related sets of analysis. First, the relationship over time between AVHRR estimated surface temperature (EST) and screen-level air temperature records is investigated and quantified. The resulting relationships are then used to produce an empirical model that can predict spatially complete summer-season air temperi atures for London. Cross-validation testing of the model at selected London weather stations showed model root mean square error (RMSE) ranging from 2.70 to 2.94°C and absolute errors in air temperature estimation of 0.45 to 1.67°C. A key finding of the thesis is that the minimal variation in prediction error between the different stations indicate a level of spatial robustness in the model across the urban surface, that is within the limits of the AVHRR EST precision. In addition, the model was used to estimate spatially averaged air temperatures over the Greater London area for selected summers, and showed a maximum error in air temperature prediction of 1.44°C. Furthermore, the prediction error for the heatwave summer of 2003 was 0.51°C, suggesting that such a model can successfully be used to estimate air temperatures for extreme heatwave summers. Such predictions are directly relevant to future assessments of urban population exposure to heatwaves, and it is envisaged that they could be used in conjunction with a population vulnerability index to create a spatially complete heatwave risk map for London. This work is then extended to investigate the utility of satellite estimated surface temperature measurements to characterise temporally and spatially intra-urban heatwave dynamics using the commonly employed urban heat island intensity metric (UHII). Analysis of the AVHRR EST found that the data are highly sensitive to local meteorological conditions, and that temporal aggregation at the monthly scale is required to provide robust data-sets for inter-year analysis of summer temperatures and generation of the UHII metric. Statistical testing of EST and air-temperature derived UHII for the heatwave summer of 2003 against other non-heatwave summers showed no significant increase in intensity at the 95% confidence level. This raises questions as to the applicability of the UHII metric to capture increases in urban temperatures during a heatwave event.Engineering and Physical Sciences Research Council and the School of Civil Engineering and Geoscience

Infrastructure planning through geosocial intelligence: using Twitter as a platform for rapid assessment and civic co-management during flooding in Jakarta

The ability to collect data using sensor-based technologies is increasing within a public technical means. As governments in rapidly-urbanising developing nations seek to address the climatic, social and economic challenges of the 21st century, there is a progressive requirement to map and articulate civil infrastructure. When a local government needs to proactively react to impending and disruptive phenomena they increasingly look to data and technology to help them manage and respond accordingly. Mobile social media, in a citizens-as-sensors paradigm, offers the potential to collect data with which to advance our capacity to understand and promote resilience of cities to both extreme weather events as a result of climate change and to long-term infrastructure transformation as a process of climate adaptation. Location-based social media, in a big-data context, can drive rapid assessment processes of affected areas, and emerging patterns and trends can be revealing about next steps for situational management. This paper emphasises the positive uses of smart systems, drawing on research of infrastructure analysis using geosocial intelligence, in response to seasonal flooding in the city of Jakarta, Indonesia. Using a series of real-world examples, we argue that data collected from the field can be secured, anonymised and encrypted to support improved planning and civic co-management of megacities. The factors that affect such bi-directional information flows need to be built on sound principles of basic needs, privacy, and trust at the individual, neighbourhood and city scales

Advances in Civic Co-management within the Geospatial Ecosystem Applied to Disaster Risk Management

The use of mobile devices for identifying risk and coordinating disaster response is well accepted and has been proven as a critical element in disaster risk management. As new tools, applications, and software are adopted by municipal governments and NGOs for the identification and management of urban risk, the need for greater integration of the various data they collect becomes acute. While the challenge of integrated data management is substantial, it is aided by the fact that many new tools have been developed to include an Application Programming Interface (API), which allows the machine-to-machine (i.e. automated) sharing of open data. While some proprietary platforms for the management of urban data are currently available, they are extremely costly and very limited in terms of data inputs; to date there are no open source geospatial software tools for the integrated management of various API sources to evaluate hazards for disaster response. A key to improving disaster risk management as an element of risk identification is the development of an integrated open source Decision-Support Risk Evaluation Matrix that enables: 1) automated integration of multiple geospatial and non-geosapatial API sources into a low cost, user-oriented dashboard; 2) backend database and software design for the Risk Evaluation Matrix that enables data sources to be parameterized and interrogated; 3) the development of an output API stream that allows additional secondary applications to optimize their evaluations and analyses through open access to critical risk information. To address these challenges this paper presents an open source Risk Evaluation Matrix, currently in development, which aims to provide situational oversight of flood hazards from multiple data-sources, including social media, in the city of Jakarta, Indonesia

Network modelling for road-based fecal sludge management

Improvements in the collection and treatment of sewage are critical to reduce health and environmental hazards in rapidly urbanising informal settlements. Where sewerage infrastructure is not available, road-based faecal sludge management options are often the only alternative. However, the costs of faecal sludge transportation are often a barrier to its implementation and operation and thus it is desirable to optimise travel time from source to treatment to reduce costs. This paper presents a novel technique, employing spatial network analysis, to optimise the spatiotopological configuration of a road-based faecal sludge transportation network on the basis of travel time. Using crowd-sourced spatial data for the Kibera settlement and the surrounding city, Nairobi, a proof-of-concept network model was created simulating the transport of waste from the 158 public toilets within Kibera. The toilets are serviced by vacuum pump trucks which move faecal sludge to a transfer station, and from there a tanker transports waste to a treatment plant. The model was used to evaluate the efficiency of different network configurations, based on transportation time. The results show that the location of the transfer station is a critical factor in network optimisation, demonstrating the utility of network analysis as part of the sanitation planning process

Network modelling for road-based Faecal Sludge Management

Improvements in the collection and treatment of sewage are critical to reduce health and environmental hazards in rapidly-urbanising informal settlements. Where sewerage infrastructure is not available, road-based Fecal Sludge Management options are often the only alternative. However, the costs of fecal sludge transportation are often a barrier to their implementation and operation and thus it is desirable to optimise travel time from source to treatment to reduce costs. This paper presents a novel technique, employing spatial network analysis, to optimise the spatio-topological configuration of a road-based fecal sludge transportation network on the basis of travel time. Using crowd-sourced spatial data for the Kibera settlement and the surrounding city, Nairobi, a proof-of-concept network model was created simulating the transport of waste from the 158 public toilets within Kibera. The toilets are serviced by vacuum pump trucks which move fecal sludge to a transfer station from where a tanker transports waste to a treatment plant. The model was used to evaluate the efficiency of different network configurations, based on transportation time. The results show that the location of the transfer station is a critical factor in network optimisation, demonstrating the utility of network analysis as part of the sanitation planning process

Geosocial intelligence

Social media, driven by the explosive uptake of mobile computing, has caused a systematic shift in the structure of personal communications at a global scale [1, 2]. People around the world can now voice opinions, report on events, and connect with others, with an ease which was unthinkable in the preinternet age. From the Arab Spring to the Occupy Movement it is apparent that social media is becoming an integrated part of our infrastructure. Critically, much of this information is underpinned by geographical content such as mobile device GPS coordinates, which enable the user to tie their media to a specific location on the Earth\u27s surface. Due to the immediacy of social medi

Postnatural urbanism in Jakarta: geosocial intelligence and the future of urban resilience

As cities evolve to become increasingly complex systems of people and interconnected infrastructure, the impacts of both extreme and long-term environmental change are significantly heightened. Understanding the resilience of urban systems and communities in an integrated manner is key to ensure the future sustainability of cities, which face considerable climatic, economic, and sociodemographic challenges in the 21st century. As Southeast Asia\u27s most populous and most dense metropolitan conurbation, and the second largest urban footprint in the world, Jakarta\u27s residents are exposed to rapid transformations of urban structures and systems

PetaJakarta.org Major Open Data Collection - Twitter activity related to flooding in Jakarta, Indonesia

Counts of geolocated tweets containing the word ‘flood’ or ‘banjir’ within the city of Jakarta, Indonesia for the 2014/2015 monsoon season. Counts were created within ‘RW’ municipal areas at hourly intervals, and include confirmed reports of flooding sent by members of the public to the @petajkt twitter account, as well as other unconfirmed tweets which match specified keywords. Keyword matching is based on substring pattern matching, and so can include tweets where a keyword is part of another word or hashtag. Data captured between 01/12/2014 - 31/03/201

From social media to geosocial intelligence: experiments with crowdsourcing civic co-management for flood response in Jakarta, Indonesia

Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management

An evaluation of thermal Earth observation for characterizing urban heatwave event dynamics using the urban heat island intensity metric

Urban areas have a high sensitivity to extreme temperature events such as heatwaves due to increased absorption and re-radiation of thermal energy from man-made materials as well as anthropogenic heat outputs. Variations in urban form, land use, and surface cover result in spatial variability in temperatures across urban areas, meaning that exposure to extreme events is variable at the sub-city scale. Such variability must be quantified in order to better understand urban temperature interactions and identify areas with the greatest potential exposure to extreme heatwave events. Earth observed data offer a spatially complete and homogenous data source to supplement observations from sparse weather station networks in order to quantify the spatial temperature variability across cities. This article presents an evaluation of the thermal data acquired by the Advanced Very High Resolution Radiometer (AVHRR) instrument to quantify the spatial temperature dynamics of London. A total of 81 cloud-free AVHRR scenes from summers between 1996 and 2006 were analysed in association with air temperature measurements from four London weather stations in order to characterize the year-on-year temperature dynamics of London. The data were employed to investigate the viability of using AVHRR scenes to distinguish a heatwave year from background years using the commonly employed urban heat island intensity (UHII) metric. Results show that AVHRR thermal data are highly sensitive to local meteorological and diurnal effects, requiring temporal averaging to the monthly and seasonal scales to provide robust data for a comparison between different years. Resulting UHII scenes highlight the spatial variability of intensity across London. However, comparison of UHII scenes between summers indicates that the UHII metric is a relatively poor means by which to distinguish between a heatwave summer in London and the 75th percentile, median, and 25th percentile summer temperatures of the time series investigated