The exploration of human activity zones using geo-tagged big data during the COVID-19 first lockdown in London, UK

Exploring the human activity zones (HAZs) gives significant insights into understanding the complex urban environment and reinforcing urban management and planning. Though previous studies have reported the significant human activity shifting at the city-level in global metropolises due to COVID-19 containment policies, the dynamic of human activity across urban areas at space and time during such an ever-changing socioeconomic period has not been examined and discussed hitherto. In this study, we proposed an analysis framework to explore the human activities zones using geo-tagged big data in London, UK. We first utilised the activity- detection method to extract visits/stops at space and time as the human activity metric from the mobile phone GPS trajectory data. Then, we characterised HAZs based on the homogeneity of hourly human activity footfalls on the middle layer super output areas (MSOAs). The results show the HAZs not only exhibit declines in human activity but are strongly associated with urban land-use and population variables during the COVID-19 pandemic

Integrating remote sensing and geospatial big data for urban land use mapping: a review

Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis

New Pathways to support social-ecological Systems in Change

Klimawandel und Biodiversitätsverlust sowie Verstädterung und demografischer Wandel haben tiefgreifende Auswirkungen auf Städte und ihre Ökosysteme und damit auf die Lebensbedingungen der Mehrheit der Menschheit. Die Geschwindigkeit des Wandels und die Dringlichkeit der Folgen macht Umweltmonitoring zu einem potentiell interessanten Tool für nachhaltige und resiliente Stadtentwicklung. Der erste Artikel gibt einen Überblick über den aktuellen Stand der Fernerkundung in Bezug auf Stadtökologie und zeigt, dass Fernerkundung relevant für nachhaltige Stadtplanung ist. Es bestehen jedoch bestehen Mängel, da viele Studien nicht direkt umsetzbar sind. Der zweite Artikel zeigt, dass eine wachsende Stadt Möglichkeiten für den Ausbau der grünen Infrastruktur bieten kann. Im dritten Artikel wird untersucht, wie sich die städtische Dichte auf die Bereitstellung von Ökosystemdienstleistungen der grünen Infrastruktur auswirkt. Es wird gezeigt, dass eine hohe Siedlungsdichte nicht zwangsläufig zu einem geringeren Biodiversitätspotenzial oder einer geringeren Kühlkapazität führt. Allerdings sind dicht bebaute Gebiete mit geringer Vegetationsbedeckung besonders auf grüne Infrastruktur angewiesen. Der vierte Artikel befasst sich mit der Frage, wie naturbasierte Lösungen durch eine bessere Vernetzung der Beteiligten gestärkt werden können. Auf der Grundlage einer gezielten Literaturrecherche über Informationstechnologie zur Unterstützung sozial-ökologischer Systeme wird ein Instrument zur Entscheidungshilfe entwickelt. Dieses kombiniert ökologische und soziale Indikatoren, um Klimawandeladaption in Übereinstimmung mit den sozio-ökologischen Bedingungen entwickeln zu können. Der fünfte Artikel bietet eine grundsätzliche Perspektive zur Unterstützung der städtischen Nachhaltigkeit, die auf dem ökologischen-Trait Konzept basiert. Zusammen bieten die fünf Artikel Wege für die Fernerkundungswissenschaft und die angewandte Raumplanung für nachhaltige und resiliente Entwicklungen in Städten.Climate change and biodiversity loss, as well as urbanisation and demographic change, are major global challenges of the 21st century. These trends have profound impacts on cities and their ecosystems and thus on the living conditions of the majority of humanity. This raises the need for timely environmental monitoring supporting sustainable and resilient urban developments. The first article is an overview of the state of the art of remote sensing science in relation to urban ecology. The review found that remote sensing can contribute to sustainable urban policy, still insufficiencies remain as many studies are not directly actionable. The second article shows that a growing city can provide opportunities for an increase in green infrastructure. Here, remote sensing is used for long-term analysis of land-use in relation to urban forms in Berlin. The third article examines how urban density affects ecosystem service provision of urban green infrastructure. It is shown that residential density does not necessarily lead to poor biodiversity potential or cooling capacity. However, dense areas with low vegetation cover are particularly dependent on major green infrastructure. The fourth article explores ways to reinforce nature-based solutions by better connecting and informing stakeholders. Based on a focussed literature review on information technology supporting urban social-ecological systems, a decision support tool is developed. The tool combines indicators based on ecological diversity and performance with population density and vulnerability. This way, climate change adaptation can be developed in accordance with socio-ecological conditions. The concluding fifth article offers an outlook on a larger framework in support of urban sustainability, based on the ecological trait concept. Together the five research papers provide pathways for urban remote sensing science and applied spatial planning that can support sustainable and resilient developments in cities

Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy

Integrating aerial and street view images for urban land use classification

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances

A NEW HIERARCHICAL CLUSTERING APPROACH FOR SPARSE MOBILE PHONE TRAJECTORIES

Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users’ one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space

The Opportunities for Digital Conservation in Making Smarter Cities More Natural

Globally, the pace of urbanisation is increasing with 68 % of the world’s population projected to be urban by 2050. The quality of life for and engagement of these urban citizens is coming to the fore as a key issue for urban planners and decision makers as they pursue place-based initiatives to optimise city performance and sustainability credentials. Decision makers tackling multiple urban discourses such as the Smart City and Natural (or green or biophilic) Cities, which now heavily influence research policy and practice agendas, find themselves addressing tricky inter-disciplinary problems. Traditional sectoral silo approaches often hinder integration, reduce the quality of the natural environment and so often fail to deliver the multiple benefits expected by communities. Drawing on a desk based systematic review of the evidence base and a workshop involving key city stakeholders, we consider how digital conservation might contribute to new integrated approaches that consider people and nature together to contribute to the wider need to deliver public services in more innovative ways within cities that are both smart and natural. We suggest that added value is identified through the combination of new technologies and this potential new governance framework, to evidence and deliver the benefits of nature for urban citizens. This builds in citizen engagement for enhancing the natural environment by embedding and exploiting the potential of digital solutions. We comment on the strengths and weaknesses of how this new conceptual approach can improve integration of service delivery, whether it can also help overcome some of the problems and risks associated with digital conservation and if, by encouraging innovation through participatory governance, also help to inform the design a more inclusive smarter and natural city. Key words: smart, natural, digital conservation, green, co-production

ANÁLISIS COMPARATIVO DE ASERTIVIDAD PARA TRES ÍNDICES DE ZONAS CONSTRUIDAS APLICADOS A CIUDADES COLOMBIANA

The delimitation and Spatio-temporal characterization of built-up or urbanized areas in cities is a fundamental input forterritorial planning. Built-up Zone Indices are used to identify urban areas using remote sensing. The objective of thisstudy was to evaluate the multitemporal assertiveness (1997, 2002, 2007 and 2018) of three Built-up Zone Indices (NDBI,UI and IBI) calculated in Landsat images for three Colombian cities. The images were enhanced through Remote Sensingtechniques and the Built-up Zone Indices were determined by taking into account the parameters established by theircreators. It means that 700 ground truth points (350 for the built-up zone and 350 for the non-built-up zone) were used to establish the multitemporal assertiveness using the Kappa Index. The results show that the index with the best overall multitemporal assertiveness was the NDBI (Kappa = 0.382), which was also the best performing for the largest city (Kappa = 0.566); for the intermediate size city, the most successful index was the UI (Kappa = 0.545). The evaluated indexes had nullKappa values in the city of Espinal; discarding the results obtained in the latter city, the global assertiveness of the indexes can be increased to 0.573. Further research is needed to evaluate in detail the applicability and assertiveness of the indicesin the Colombian context, as well as the adjustments to the optimal value range for each particular city according to its architectural characteristics.La delimitación y caracterización espacio-temporal de las zonas construidas o urbanizadas en las ciudades es un insumo fundamental para la planificación territorial. Los Índices de Zonas Construidas son empleados para identificar las zonasurbanas utilizando sensores remotos. Este estudio tuvo por objetivo evaluar la asertividad multitemporal (1997, 2002, 2007 y 2018) de tres Índices de Zonas Construidas (NDBI, UI e IBI) calculados en imágenes Landsat para tres ciudadescolombianas. Las imágenes fueron mejoradas a través de técnicas de Teledetección y se determinaron los Índices de Zonas Construidas teniendo en cuenta los parámetros establecidos por sus  creadores. Se emplearon 700 puntos verdad terreno(350 para zonas construidas y 350 para zonas no construidas) para establecer la asertividad multitemporal usando el Índice de Kappa. Los resultados muestran que el índice con mejor asertividad multitemporal global fue el NDBI (Kappa = 0.382),el cual también fue el de mejor desempeño para la ciudad de mayor tamaño (Kappa = 0.566); para la ciudad de tamaño intermedio el índice más acertado correspondió al UI (Kappa = 0.545). Los Índices evaluados tuvieron valores nulos de Kappa en la ciudad de Espinal; descartando los resultados obtenidos en esta última ciudad, la asertividad global de losíndices puede incrementarse hasta 0.573. Se infiere la necesidad de realizar nuevas investigaciones que permitan evaluar amayor detalle la aplicabilidad y asertividad de estos índices en el contexto colombiano, al igual que los ajustes a los rangos de valores óptimos para cada ciudad en particular de acuerdo a sus características arquitectónicas

Uso y cobertura del suelo en las islas macaronésicas de Portugal y España: nuevos métodos para cuantificar y visualizar información de patrones espaciales

Tesis de la Universidad Complutense de Madrid, Facultad de Geografía e Historia, Departamento de Geografía Humana, leída el 23/11/2016The aim of this research is to propose novel methods for quantifying and visualizing geographical information, in order to aid the spatial planning decision-making process when addressing land use and land cover patterns. In doing so, several modeling and geographic visualization methods are developed and demonstrated by using the Macaronesian islands of Portugal and Spain as study areas. Macaronesia is a biogeographical region consisting of several archipelagos in the Atlantic Ocean belonging to three countries: Portugal, Spain, and Cape Verde. This research encompasses three archipelagos: the Azores, Madeira, and the Canary Islands. From these three archipelagos, the four most densely populated islands were further selected for the land use and land cover assessments: São Miguel, Madeira, Tenerife, and Gran Canaria. A common feature of the Macaronesian islands is that, ever since European colonization in the fifteenth century, up until the mid-twentieth century, anthropogenic land change was predominately attributable to agricultural activities consuming forests and natural areas. In the mid-twentieth century, owing to profound social and economic changes, the tertiary sector started its rise in becoming the main economic sector. Because the secondary sector in this region has always been minor, this substantial shift to the tertiary sector would dictate a progressive abandonment of the primary sector. Hence, agricultural areas started to recede. As a result, the last decades of the twentieth century were marked by a significant shift in land use dynamics. Agricultural activities ceased to be the main driving force of land change and were replaced by a rampant increase of the artificial surfaces, mainly on the southern coastal areas, where tourism-related and real estate pressure constitute a major impact on the landscape. A direct consequence of this pressure was the drastic transformation across the islands’ leeward coastal landscapes...El objetivo principal de esta investigación es proponer nuevos métodos para cuantificar y visualizar información geográfica, con el fin de facilitar el proceso de toma de decisiones en relación a los patrones de uso y ocupación del suelo. De este modo, se desarrollan y aplican varios métodos de modelación y visualización geográfica, utilizando las islas macaronésicas de Portugal y España como áreas de estudio. La Macaronesia es una región biogeográfica que integra varios archipiélagos en el Océano Atlántico pertenecientes a tres países: Portugal, España y Cabo Verde. Esta investigación abarca tres archipiélagos: Azores, Madeira y Canarias. Para una evaluación detallada de uso y cobertura del suelo se seleccionaron las cuatro islas más densamente pobladas: San Miguel, Madeira, Tenerife y Gran Canaria. Una característica común a las islas macaronésicas es que, desde de la colonización en el siglo XV hasta mediados del siglo XX, el cambio antropogénico del suelo se debió principalmente a las actividades agrícolas, que ocuparon bosques y áreas naturales. A mediados del siglo XX, debido a profundos cambios sociales y económicos, el sector terciario empezó su ascenso para convertirse en el principal sector económico. Debido a que el sector secundario en esta región siempre ha tenido una importancia menor, este proceso de terciarización de la economía supuso un progresivo abandono del sector primario. Por lo tanto, las áreas agrícolas comenzaron a experimentar un claro retroceso. Como resultado de este proceso, las últimas décadas del siglo XX se caracterizaron por un cambio significativo en las dinámicas de uso y cobertura del suelo. Las actividades agrícolas dejaron de ser la principal fuerza impulsora en el cambio de lo suelo y fueron reemplazadas por el aumento desenfrenado de las superficies artificiales, principalmente en las zonas costeras del sur, donde el turismo y la especulación inmobiliaria ejercen una gran presión sobre el paisaje. Consecuencia directa de esta presión fueron las drásticas transformaciones de los paisajes costeros de las islas...Esta investigação tem como principal objectivo propor novos métodos para quantificar e visualizar informação geográfica, de modo a auxiliar o processo de tomada de decisão quando seja necessário analisar padrões de uso e ocupação do solo. Ao longo da investigação são apresentados vários métodos de modelação e visualização geográfica, usando como área de estudo as ilhas da Macaronésia pertencentes a Portugal e Espanha. A Macaronésia é uma região biogeográfica no Oceano Atlântico constituída por vários arquipélagos pertencentes a três países: Portugal, Espanha e Cabo Verde. Este trabalho de investigação abrange três arquipélagos: os Açores, a Madeira e as Ilhas Canárias. Para uma avaliação mais detalhada quanto ao uso e ocupação do solo, foram seleccionadas as quatro ilhas mais densamente povoadas: São Miguel, Madeira, Gran Canaria e Tenerife. Uma característica comum às ilhas da Macaronésia reside na particularidade de, desde a sua colonização no século XV, até meados do século XX, as alterações antropogénicas do solo terem estado predominantemente associadas às actividades agrícolas que consumiram extensas áreas de floresta e espaços naturais. Em meados do século XX, devido a profundas alterações sociais e económicas, o sector terciário iniciou a sua ascensão para se tornar o principal sector económico. Uma vez que, nesta região, o sector secundário foi sempre pouco significativo, a terciarização da actividade económica ditou um progressivo abandono do sector primário. Deste modo, as áreas agrícolas começaram a recuar. Como resultado deste processo, as últimas décadas do século XX foram marcadas por uma mudança significativa na dinâmica de uso e ocupação do solo nas ilhas desta região. As actividades agrícolas deixaram de ser a principal força motriz para as alterações no uso do solo, sendo substituídas pelo aumento galopante das superfícies artificiais, principalmente nas áreas costeiras do sul, onde as actividades relacionadas com o turismo e a especulação imobiliária causaram um grande impacto na paisagem, e contribuiram para a transformação drástica do litoral sotavento das ilhas...Depto. de GeografíaFac. de Geografía e HistoriaTRUEunpu

Une approche basée graphes pour la détection de zones fonctionnelles urbaines

International audienceDans cet article, nous proposons une méthode pour l'identification de zones fonctionnelles, utilisant la détection de communautés dans un graphe de mobilité. Les sommets du graphe correspondent à des unités spatiales, issues du découpage d'une ville suivant le réseau routier. Les arêtes relient des sommets entres lesquels des déplacements sont observés et sont pondérées en fonction du nombre de déplacements et de la distance entre sommets. Notre approche optimise la modularité sur ce réseau pour assurer que les zones fonctionnelles obtenues maximisent les interactions spatiales en leur sein. De plus, nous uti-lisons les points d'intérêts pour maintenir une hétérogénéité suffisante dans les zones détectées. Nous avons mené des expérimentations avec des trajectoires de taxi et des points d'intérêts de la ville de Porto, afin de montrer la capacité de notre approche à identifier les zones fonctionnelles