‘Healthy mobile check-ins’ study: using GIS in smartphones to track use of urban environment by survivors of endometrial cancer

Aims/Objectives Our study aims to test the capacity of a newly developed smartphone innovation to obtain data on social, structural, and spatial determinants of the daily health-related behaviours of women living in urban Brisbane neighbourhoods who have survived endometrial cancer. Methods The women used a mobile web app designed specifically for the project to record GIS/location data on every destination they visited within their local urban neighbourhoods over a two-week period. Additionally, we gathered textual data on the social context/reasons for travel, as well as mode of transport to reach these destinations. The data was transported to SPSS and Google Earth for statistical and spatial analysis. We then met with the women to discuss lifestyle interventions to maximise their use of their local neighbourhoods in ways that could increase their physical activity levels and improve their overall health and well-being. These interventions will be evaluated and translated into a large-scale national study if effective. Results Initial findings about patterns in the group’s use of the local urban environment will be displayed, including daily distances travelled, types of locations visited, walking levels, use of public transport, use of green spaces and use of health-related resources. Any socio-demograpahic differences found between the women will be reported. Qualitative, quantitative, and spatial/mapping data will be displayed Conclusion The benefits and limitations of the mobile website designed to collect a range of data types about human-neighbourhood interactions with implications for intervention design will be discussed.

The use of context in the classification of urban aerial imagery

Urban regions present some of the most challenging areas for the remote sensing community. Many different types of land cover have similar spectral responses, making them difficult to distinguish from one another. Traditional per-pixel classification techniques suffer particularly badly because they only use these spectral properties to determine a class, and no other properties of the image, such as context. This project presents the results of the classification of a deeply urban area of Dudley, West Midlands, using 4 methods: Supervised Maximum Likelihood, SMAP, ECHO and Unsupervised Maximum Likelihood. An accuracy assessment method is then developed to allow a fair representation of each procedure and a direct comparison between them. Subsequently, a classification procedure is developed that makes use of the context in the image, though a per-polygon classification. The imagery is broken up into a series of polygons extracted from the Marr-Hildreth zero-crossing edge detector. These polygons are then refined using a region-growing algorithm, and then classified according to the mean class of the fine polygons. The imagery produced by this technique is shown to be of better quality and of a higher accuracy than that of other conventional methods. Further refinements are suggested and examined to improve the aesthetic appearance of the imagery. Finally a comparison with the results produced from a previous study of the James Bridge catchment, in Darleston, West Midlands, is made, showing that the Polygon classified ATM imagery performs significantly better than the Maximum Likelihood classified videography used in the initial study, despite the presence of geometric correction errors.

Urban growth monitoring techniques for sustainable development

This paper discusses the role of advance techniques for monitoring urban growth and change for sustainable development of urban environment. It also presents results of a case study involving satellite data for land use/land cover classification of Lucknow city using IRS-1C multi-spectral features. Two classification algorithms have been used in the study. Experiments were conducted to see the level of improvement in digital classification of urban environment using Artificial Neural Network (ANN) technique.

Modelling the Impact of Built Environment, Geographical Scales and Latent Constructs On Individual Travel Behaviour

La relación entre la estructura urbana y la movilidad ha sido estudiada desde hace más de 70 años. El entorno urbano incluye múltiples dimensiones como por ejemplo: la estructura urbana, los usos de suelo, la distribución de instalaciones diversas (comercios, escuelas y zonas de restauración, parking, etc.). Al realizar una revisión de la literatura existente en este contexto, se encuentran distintos análisis, metodologías, escalas geográficas y dimensiones, tanto de la movilidad como de la estructura urbana. En este sentido, se trata de una relación muy estudiada pero muy compleja, sobre la que no existe hasta el momento un consenso sobre qué dimensión del entorno urbano influye sobre qué dimensión de la movilidad, y cuál es la manera apropiada de representar esta relación. Con el propósito de contestar estas preguntas investigación, la presente tesis tiene los siguientes objetivos generales: (1) Contribuir al mejor entendimiento de la compleja relación estructura urbana y movilidad. y (2) Entender el rol de los atributos latentes en la relación entorno urbano y movilidad. El objetivo específico de la tesis es analizar la influencia del entorno urbano sobre dos dimensiones de la movilidad: número de viajes y tipo de tour. Vista la complejidad de la relación entorno urbano y movilidad, se pretende contribuir al mejor entendimiento de la relación a través de la utilización de 3 escalas geográficas de las variables y del análisis de la influencia de efectos inobservados en la movilidad. Para el análisis se utiliza una base de datos conformada por tres tipos de datos: (1) Una encuesta de movilidad realizada durante los años 2006 y 2007. Se obtuvo un total de 943 encuestas, en 3 barrios de Madrid: Chamberí, Pozuelo y Algete. (2) Información municipal del Instituto Nacional de Estadística: dicha información se encuentra enlazada con los orígenes y destinos de los viajes recogidos en la encuesta. Y (3) Información georeferenciada en Arc-GIS de los hogares participantes en la encuesta: la base de datos contiene información respecto a la estructura de las calles, localización de escuelas, parking, centros médicos y lugares de restauración. Se analizó la correlación entre e intra-grupos y se modelizaron 4 casos de atributos bajo la estructura ordinal logit. Posteriormente se evalúa la auto-selección a través de la estimación conjunta de las elecciones de tipo de barrio y número de viajes. La elección del tipo de barrio consta de 3 alternativas: CBD, Urban y Suburban, según la zona de residencia recogida en las encuestas. Mientras que la elección del número de viajes consta de 4 categorías ordinales: 0 viajes, 1-2 viajes, 3-4 viajes y 5 o más viajes. A partir de la mejor especificación del modelo ordinal logit. Se desarrolló un modelo joint mixed-ordinal conjunto. Los resultados indican que las variables exógenas requieren un análisis exhaustivo de correlaciones con el fin de evitar resultados sesgados. ha determinado que es importante medir los atributos del BE donde se realiza el viaje, pero también la información municipal es muy explicativa de la movilidad individual. Por tanto, la percepción de las zonas de destino a nivel municipal es considerada importante. En el contexto de la Auto-selección (self-selection) es importante modelizar conjuntamente las decisiones. La Auto-selección existe, puesto que los parámetros estimados conjuntamente son significativos. Sin embargo, sólo ciertos atributos del entorno urbano son igualmente importantes sobre la elección de la zona de residencia y frecuencia de viajes. Para analizar la Propensión al Viaje, se desarrolló un modelo híbrido, formado por: una variable latente, un indicador y un modelo de elección discreta. La variable latente se denomina “Propensión al Viaje”, cuyo indicador en ecuación de medida es el número de viajes; la elección discreta es el tipo de tour. El modelo de elección consiste en 5 alternativas, según la jerarquía de actividades establecida en la tesis: HOME, no realiza viajes durante el día de estudio, HWH tour cuya actividad principal es el trabajo o estudios, y no se realizan paradas intermedias; HWHs tour si el individuo reaiza paradas intermedias; HOH tour cuya actividad principal es distinta a trabajo y estudios, y no se realizan paradas intermedias; HOHs donde se realizan paradas intermedias. Para llegar a la mejor especificación del modelo, se realizó un trabajo importante considerando diferentes estructuras de modelos y tres tipos de estimaciones. De tal manera, se obtuvieron parámetros consistentes y eficientes. Los resultados muestran que la modelización de los tours, representa una ventaja sobre la modelización de los viajes, puesto que supera las limitaciones de espacio y tiempo, enlazando los viajes realizados por la misma persona en el día de estudio. La propensión al viaje (PT) existe y es específica para cada tipo de tour. Los parámetros estimados en el modelo híbrido resultaron significativos y distintos para cada alternativa de tipo de tour. Por último, en la tesis se verifica que los modelos híbridos representan una mejora sobre los modelos tradicionales de elección discreta, dando como resultado parámetros consistentes y más robustos. En cuanto a políticas de transporte, se ha demostrado que los atributos del entorno urbano son más importantes que los LOS (Level of Service) en la generación de tours multi-etapas. la presente tesis representa el primer análisis empírico de la relación entre los tipos de tours y la propensión al viaje. El concepto Propensity to Travel ha sido desarrollado exclusivamente para la tesis. Igualmente, el desarrollo de un modelo conjunto RC-Number of trips basado en tres escalas de medida representa innovación en cuanto a la comparación de las escalas geográficas, que no había sido hecha en la modelización de la self-selection. The relationship between built environment (BE) and travel behaviour (TB) has been studied in a number of cases, using several methods - aggregate and disaggregate approaches - and different focuses – trip frequency, automobile use, and vehicle miles travelled and so on. Definitely, travel is generated by the need to undertake activities and obtain services, and there is a general consensus that urban components affect TB. However researches are still needed to better understand which components of the travel behaviour are affected most and by which of the urban components. In order to fill the gap in the research, the present dissertation faced two main objectives: (1) To contribute to the better understanding of the relationship between travel demand and urban environment. And (2) To develop an econometric model for estimating travel demand with urban environment attributes. With this purpose, the present thesis faced an exhaustive research and computation of land-use variables in order to find the best representation of BE for modelling trip frequency. In particular two empirical analyses are carried out: 1. Estimation of three dimensions of travel demand using dimensions of urban environment. We compare different travel dimensions and geographical scales, and we measure self-selection contribution following the joint models. 2. Develop a hybrid model, integrated latent variable and discrete choice model. The implementation of hybrid models is new in the analysis of land-use and travel behaviour. BE and TB explicitly interact and allow richness information about a specific individual decision process For all empirical analysis is used a data-base from a survey conducted in 2006 and 2007 in Madrid. Spatial attributes describing neighbourhood environment are derived from different data sources: National Institute of Statistics-INE (Administrative: municipality and district) and GIS (circular units). INE provides raw data for such spatial units as: municipality and district. The construction of census units is trivial as the census bureau provides tables that readily define districts and municipalities. The construction of circular units requires us to determine the radius and associate the spatial information to our households. The first empirical part analyzes trip frequency by applying an ordered logit model. In this part is studied the effect of socio-economic, transport and land use characteristics on two travel dimensions: trip frequency and type of tour. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Three neighbourhood representations are explored, and described three for constructing neighbourhood attributes. In particular administrative units are examined to represent neighbourhood and circular – unit representation. Ordered logit models are applied, while ordinal logit models are well-known, an intensive work for constructing a spatial attributes was carried out. On the other hand, the second empirical analysis consists of the development of an innovative econometric model that considers a latent variable called “propensity to travel”, and choice model is the choice of type of tour. The first two specifications of ordinal models help to estimate this latent variable. The latent variable is unobserved but the manifestation is called “indicators”, then the probability of choosing an alternative of tour is conditional to the probability of latent variable and type of tour. Since latent variable is unknown we fit the integral over its distribution. Four “sets of best variables” are specified, following the specification obtained from the correlation analysis. The results evidence that the relative importance of SE variables versus BE variables depends on how BE variables are measured. We found that each of these three spatial scales has its intangible qualities and drawbacks. Spatial scales play an important role on predicting travel demand due to the variability in measures at trip origin/destinations within the same administrative unit (municipality, district and so on). Larger units will produce less variation in data; but it does not affect certain variables, such as public transport supply, that are more significant at municipality level. By contrast, land-use measures are more efficient at district level. Self-selection in this context, is weak. Thus, the influence of BE attributes is true. The results of the hybrid model show that unobserved factors affect the choice of tour complexity. The latent variable used in this model is propensity to travel that is explained by socioeconomic aspects and neighbourhood attributes. The results show that neighbourhood attributes have indeed a significant impact on the choice of the type of tours either directly and through the propensity to travel. The propensity to travel has a different impact depending on the structure of each tour and increases the probability of choosing more complex tours, such as tours with many intermediate stops. The integration of choice and latent variable model shows that omitting important perception and attitudes leads to inconsistent estimates. The results also indicate that goodness of fit improves by adding the latent variable in both sequential and simultaneous estimation. There are significant differences in the sensitivity to the latent variable across alternatives. In general, as expected, the hybrid models show a major improvement into the goodness of fit of the model, compared to a classical discrete choice model that does not incorporate latent effects. The integrated model leads to a more detailed analysis of the behavioural process. Summarizing, the effect that built environment characteristics on trip frequency studied is deeply analyzed. In particular we tried to better understand how land use characteristics can be defined and measured and which of these measures do have really an impact on trip frequency. We also tried to test the superiority of HCM on this field. We can concluded that HCM shows a major improvement into the goodness of fit of the model, compared to classical discrete choice model that does not incorporate latent effects. And consequently, the application of HCM shows the importance of LV on the decision of tour complexity. People are more elastic to built environment attributes than level of services. Thus, policy implications must take place to develop more mixed areas, work-places in combination with commercial retails.

Análise da incerteza na representação de classes de cobertura do solo urbano resultantes da aplicação de uma rede neural artificial

The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.

Análise da incerteza na representação de classes temáticas resultantes da aplicação de uma rede neural artificial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An investigation of nucleation events in a coastal urban environment in the southern hemisphere

The occurrence of and conditions favourable to nucleation were investigated at an industrial and commercial coastal location in Brisbane, Australia during five different campaigns covering a total period of 13 months. To identify potential nucleation events, the difference in number concentration in the size range 14-30 nm (N14-30) between consecutive observations was calculated using first-order differencing. The data showed that nucleation events were a rare occurrence, and that in the absence of nucleation the particle number was dominated by particles in the range 30-300 nm. In many instances, total particle concentration declined during nucleation. There was no clear pattern in change in NO and NO2 concentrations during the events. SO2 concentration, in the majority of cases, declined during nucleation but there were exceptions. Most events took place in summer, followed by winter and then spring, and no events were observed for the autumn campaigns. The events were associated with sea breeze and long-range transport. Roadside emissions, in contrast, did not contribute to nucleation, probably due to the predominance of particles in the range 50-100 nm associated with these emissions.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

Turbulent velocity and suspended sediment concentration measurements in an urban environment of the Brisbane River Flood Plain at Gardens Point on 12-13 January 2011

The flood flow in urbanised areas constitutes a major hazard to the population and infrastructure as seen during the summer 2010-2011 floods in Queensland (Australia). Flood flows in urban environments have been studied relatively recently, although no study considered the impact of turbulence in the flow. During the 12-13 January 2011 flood of the Brisbane River, some turbulence measurements were conducted in an inundated urban environment in Gardens Point Road next to Brisbane's central business district (CBD) at relatively high frequency (50 Hz). The properties of the sediment flood deposits were characterised and the acoustic Doppler velocimeter unit was calibrated to obtain both instantaneous velocity components and suspended sediment concentration in the same sampling volume with the same temporal resolution. While the flow motion in Gardens Point Road was subcritical, the water elevations and velocities fluctuated with a distinctive period between 50 and 80 s. The low frequency fluctuations were linked with some local topographic effects: i.e, some local choke induced by an upstream constriction between stairwells caused some slow oscillations with a period close to the natural sloshing period of the car park. The instantaneous velocity data were analysed using a triple decomposition, and the same triple decomposition was applied to the water depth, velocity flux, suspended sediment concentration and suspended sediment flux data. The velocity fluctuation data showed a large energy component in the slow fluctuation range. For the first two tests at z = 0.35 m, the turbulence data suggested some isotropy. At z = 0.083 m, on the other hand, the findings indicated some flow anisotropy. The suspended sediment concentration (SSC) data presented a general trend with increasing SSC for decreasing water depth. During a test (T4), some long -period oscillations were observed with a period about 18 minutes. The cause of these oscillations remains unknown to the authors. The last test (T5) took place in very shallow waters and high suspended sediment concentrations. It is suggested that the flow in the car park was disconnected from the main channel. Overall the flow conditions at the sampling sites corresponded to a specific momentum between 0.2 to 0.4 m2 which would be near the upper end of the scale for safe evacuation of individuals in flooded areas. But the authors do not believe the evacuation of individuals in Gardens Point Road would have been safe because of the intense water surges and flow turbulence. More generally any criterion for safe evacuation solely based upon the flow velocity, water depth or specific momentum cannot account for the hazards caused by the flow turbulence, water depth fluctuations and water surges.

Physical characteristics of small ions and their interaction with ultrafine particles in the urban environment

In recent years, the effect of ions and ultrafine particles on ambient air quality and human health has been well documented, however, knowledge about their sources, concentrations and interactions within different types of urban environments remains limited. This thesis presents the results of numerous field studies aimed at quantifying variations in ion concentration with distance from the source, as well as identifying the dynamics of the particle ionisation processes which lead to the formation of charged particles in the air. In order to select the most appropriate measurement instruments and locations for the studies, a literature review was also conducted on studies that reported ion and ultrafine particle emissions from different sources in a typical urban environment. The initial study involved laboratory experiments on the attachment of ions to aerosols, so as to gain a better understanding of the interaction between ions and particles. This study determined the efficiency of corona ions at charging and removing particles from the air, as a function of different particle number and ion concentrations. The results showed that particle number loss was directly proportional to particle charge concentration, and that higher small ion concentrations led to higher particle deposition rates in all size ranges investigated. Nanoparticles were also observed to decrease with increasing particle charge concentration, due to their higher Brownian mobility and subsequent attachment to charged particles. Given that corona discharge from high voltage powerlines is considered one of the major ion sources in urban areas, a detailed study was then conducted under three parallel overhead powerlines, with a steady wind blowing in a perpendicular direction to the lines. The results showed that large sections of the lines did not produce any corona at all, while strong positive emissions were observed from discrete components such as a particular set of spacers on one of the lines. Measurements were also conducted at eight upwind and downwind points perpendicular to the powerlines, spanning a total distance of about 160m. The maximum positive small and large ion concentrations, and DC electric field were observed at a point 20 m downwind from the lines, with median values of 4.4×103 cm-3, 1.3×103 cm-3 and 530 V m-1, respectively. It was estimated that, at this point, less than 7% of the total number of particles was charged. The electrical parameters decreased steadily with increasing downwind distance from the lines but remained significantly higher than background levels at the limit of the measurements. Moreover, vehicles are one of the most prevalent ion and particle emitting sources in urban environments, and therefore, experiments were also conducted behind a motor vehicle exhaust pipe and near busy motorways, with the aim of quantifying small ion and particle charge concentration, as well as their distribution as a function of distance from the source. The study found that approximately equal numbers of positive and negative ions were observed in the vehicle exhaust plume, as well as near motorways, of which heavy duty vehicles were believed to be the main contributor. In addition, cluster ion concentration was observed to decrease rapidly within the first 10-15 m from the road and ion-ion recombination and ion-aerosol attachment were the most likely cause of ion depletion, rather than dilution and turbulence related processes. In addition to the above-mentioned dominant ion sources, other sources also exist within urban environments where intensive human activities take place. In this part of the study, airborne concentrations of small ions, particles and net particle charge were measured at 32 different outdoor sites in and around Brisbane, Australia, which were classified into seven different groups as follows: park, woodland, city centre, residential, freeway, powerlines and power substation. Whilst the study confirmed that powerlines, power substations and freeways were the main ion sources in an urban environment, it also suggested that not all powerlines emitted ions, only those with discrete corona discharge points. In addition to the main ion sources, higher ion concentrations were also observed environments affected by vehicle traffic and human activities, such as the city centre and residential areas. A considerable number of ions were also observed in a woodland area and it is still unclear if they were emitted directly from the trees, or if they originated from some other local source. Overall, it was found that different types of environments had different types of ion sources, which could be classified as unipolar or bipolar particle sources, as well as ion sources that co-exist with particle sources. In general, fewer small ions were observed at sites with co-existing sources, however particle charge was often higher due to the effect of ion-particle attachment. In summary, this study quantified ion concentrations in typical urban environments, identified major charge sources in urban areas, and determined the spatial dispersion of ions as a function of distance from the source, as well as their controlling factors. The study also presented ion-aerosol attachment efficiencies under high ion concentration conditions, both in the laboratory and in real outdoor environments. The outcomes of these studies addressed the aims of this work and advanced understanding of the charge status of aerosols in the urban environment.

Impact of urban traffic and climate change on water quality from road runoff

Urban traffic and climate change are two phenomena that have the potential to degrade urban water quality by influencing the build-up and wash-off of pollutants, respectively. However, limited knowledge has made it difficult to establish any link between pollutant buildup and wash-off under such dynamic conditions. In order to safeguard urban water quality, adaptive water quality mitigation measures are required. In this research, pollutant build-up and wash-off have been investigated from a dynamic point of view which incorporated the impacts of changed urban traffic as well as changes in the rainfall characteristics induced by climate change. The study has developed a dynamic object classification system and thereby, conceptualised the study of pollutant build-up and wash-off under future changes in urban traffic and rainfall characteristics. This study has also characterised the buildup and wash-off processes of traffic generated heavy metals, volatile, semi-volatile and non-volatile hydrocarbons under dynamic conditions which enables the development of adaptive mitigation measures for water quality. Additionally, predictive frameworks for the build-up and wash-off of some pollutants have also been developed.

Turbulence and suspended sediment measurements in an urban environment during the Brisbane River flood of January 2011

In urbanised areas, the flood flows constitute a hazard to populations and infrastructure as illustrated during major floods in 2011. During the 2011 Brisbane River flood, some turbulent velocity data were collected using acoustic Doppler velocimetry in an inundated street. The field deployment showed some unusual features of flood flow in the urban environment. That is, the water elevations and velocities fluctuated with distinctive periods between 50 and 100 s linked with some local topographic effects. The instantaneous velocity data were analysed using a triple decomposition. The velocity fluctuations included a large energy component in the slow fluctuation range, while the turbulent motion components were much smaller. The suspended sediment data showed some significant longitudinal flux. Altogether the results highlighted that the triple decomposition approach originally developed for period flows is well suited to complicated flows in an inundated urban environment.