Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis

[EN] The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.This work was supported by the the Spanish Ministerio de Economia y Competitividad and FEDER [CGL2016-80705-R].Sapena Moll, M.; Ruiz Fernández, LÁ. (2021). Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis. International Journal of Geographical Information Science. 35(2):375-396. https://doi.org/10.1080/13658816.2020.181746337539635

Analysis of land use/land cover spatio-temporal metrics and population dynamics for urban growth characterization

[EN] Promoting sustainable urbanization and limiting land consumption is a local and regional priority policy target in Europe. Monitoring and quantifying urban growth supports decision-making processes for the prevention of ecological and socio-economic consequences. In this work, we present a methodology based on spatio-temporal metrics and a new index (PUGI), that quantifies the inequality of growth between population and urban areas, to analyze and compare urban growth patterns at different levels. We computed an exhaustive set of spatio-temporal metrics at local level in a testing sample of six urban areas from the Urban Atlas database, then un-correlated metrics were selected and the data were interpreted at various levels. Results allow for a differentiation of growing patterns, discriminating between compact and sprawl trends. The index proposed complements the analysis by including demographic dynamics, being also useful for assessing the growing imbalance between the progression on residential areas and the population change at local level. The analysis at various levels contributes to a better understanding of urban growth patterns and its relation to sustainable policies not only within urban areas, but also for the comparison across Europe.This research has been funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R and the Fondo de Garantia Juvenil contract PEJ-2014-A-45358.Sapena Moll, M.; Ruiz Fernández, LÁ. (2019). Analysis of land use/land cover spatio-temporal metrics and population dynamics for urban growth characterization. Computers Environment and Urban Systems. 73:27-39. https://doi.org/10.1016/j.compenvurbsys.2018.08.001S27397

Analyzing links between spatio-temporal metrics of built-up areas and socio-economic indicators on a semi-global scale

[EN] Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future.Sapena Moll, M.; Ruiz Fernández, LÁ.; Taubenböck, H. (2020). 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Categorizing Urban Structural Types using an Object-Based Local Climate Zone Classification Scheme in Medellín, Colombia

Climate change is reshaping societies. As we see more and more people moving to urban areas an ever-increasing number settles in low-cost and more hazardous areas. However, due to the rapid growth and sheer scale of informal settlements, knowledge gaps often exist on location or quantity. In this sense, Earth Observation combined with machine learning techniques allows to generate reliable geo-information. In this study, we classify the morphologically heterogeneous entire urban area of Medellín, Colombia into urban structural types. We do this by the Local Climate Zone (LCZ) scheme. Our specific focus is on one structural type, i.e. informal settlements. We test whether it is feasible by the LCZ concept to localize and quantify these vulnerable areas. The LCZ scheme is generic, replicable, neutral, and has become widespread in urban studies. We use urban blocks to perform a scene-based image classification into nine LCZs. We refer to multi-modal remotely-sensed data: high-resolution multispectral image data and elevation data. We apply an optimized random forest algorithm using shape metrics, as well as spectral and texture features. In general, we find the LCZ classification, measured with an overall accuracy of 82%, shows a reliable representation of urban typologies and functions across the city. Specifically, we compare the urban blocks classified as the LCZ lightweigth low-rise to the informal settlements provided by the city of Medellín. Here we reach an agreement of 86%. Besides, our approach complements the official dataset by including recently developed areas which are not yet considered by the city

The growing threat: Earth Observation for reducing landslide risk from climate change

In this paper a specific case study is introduced illustrating how Earth Observation (EO) can be used to assess exposure and vulnerability to landslide hazards and to strengthen resilience. Combining heterogeneous EO data can greatly improve knowledge on natural hazard risks for many urban dwellers

The migrant perspective: Measuring migrants' movements and interests using geolocated tweets

Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community‐defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large‐scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research

Multitemporal landslide exposure and vulnerability assessment in Medellín, Colombia

Landslides are often deadly natural events. Steep slopes and certain loose soil types are predestined areas for them. Moreover, in the context of climate change, extreme weather events such as heavy rainfall, which often trigger landslides, are becoming even more likely. While all this is well known, it, therefore, stands to reason that this knowledge will lead to the avoidance of these risks. On the other hand, however, there are highly dynamic urbanization processes that often overtake formal urban planning processes by rising population figures and areal expansion. In the course of these processes, economically-deprived population groups often have no other option than to informally build on high-risk areas. Against these backgrounds, we systematically examine in this study how these risks develop over a 24-year period from 1994 to 2018 taking into account three time steps, with respect to the city-wide exposure and in particular with respect to different social groups. For this purpose, we use heterogeneous input data from remote sensing, landslide hazard maps, and census data. Our case study is the city of Medellín in Colombia. We develop and apply a set of methods integrating the heterogenous data sets to map, quantify and monitor exposure and social vulnerability at a fine spatial granularity. Our results document first of all the highly dynamic growth in total population and urban areas. However, our results reveal that the city's expansion is socially unevenly distributed. People of higher vulnerability proxied by informal settlements are found to settle in considerably higher shares of areas exposed to landslides. This study proposes a methodological set-up that allows for monitoring exposure and social vulnerability over long time spans at a fine spatial resolution, allows to bring inequality into the spotlight, and provides decision-makers with better information to develop socially responsible policies

Migrants: the pull effects of rural industrial sites as seen from space

Accessibility to raw materials, cheap labour and lenient labour laws make rural areas attractive to many industries in West Africa. The set-up of small-scale solid mineral industries is popular in rural West Africa. These industries are labour intensive and require small to large areas of land. This is just one of the examples of industrialization taking place in rural areas. Nigeria is well known for its vast oil reserves, which in turn creates a lot of employment opportunities, especially for low-skilled workers, since many of the reserves are in rural areas. Ghana's southern western region has a wealth of gold, which has caused small-scale industries to spring up and led to an influx of people from more rural areas. In combination with proximity to mineral resources, this has led to rural industrialization. This can be seen in the increase in the number of people in an area which indicates an influx of migrants. When this happens there's an upsurge in migration to rural areas, pressure on land and water resources from agricultural activities, which affects the livelihood of migrants. This study seeks to identify migrants' behaviours to move to rural industrial areas in Ghana and Nigeria using remote sensing proxies. The method will use several remote sensing products such as Landsat, Copernicus datasets, Hansen Global Forest dataset, WorldPop and JRC-Global Human Settlement Layer dataset. The Random Forest classifier will be used to generate a Landcover map of the selected areas with Copernicus and Landsat datasets. The expected result will have the potential to demonstrate that Copernicus data, World Pop and Hansen Forest Cover data can be a useful proxy for population and migration studies. Moreover, the monitored significant changes in land use and land cover in the industrial areas compared over the past 20 years reveal certain trends of the industrialization era in Western Africa. The research has the capabilities of producing effective and accurate methods for identifying the pull effects of industries in rural areas. This is essential for the implementation of policies for improved infrastructure, improved labour laws, good health and decent wages

Enhancing Earth Observation of Migration with Insights from Social Media

Complex migration processes are pervasive on the African continent and are intrinsically linked to ongoing demographic, social, economic, and ecological changes. To better understand the complexity of these processes, there is a need for more and better data and knowledge on the flows, the drivers, and the effects of migration. Earth Observation is increasingly able to accurately map drivers and effects of migration which have a visible impact on the earth’s surface. Such drivers are e.g. floods, droughts, and effects are e.g. urban expansion or refugee camps (top image: Sentinel-2 based maps of the migration induced urban growth in Abuja and Maiduguri, which takes the form of low-dense urban development and refugee camps). However, underlying socio-economic and political drivers as well as individuals‘ subjective decisions or perceptions of situations and the environment can not be directly mapped using Earth Observation sensors. In this project we aim to reduce these knowledge gaps by additional data sources. In this context, geolocated social media data can provide valuable insights into migrants’ movements and motivations

Revealing landslisde exposure of informal settlements in Medellín using Deep Learning

Large areas of informal settlements on the slopes of Medellín are exposed to landslide risk, but there exists no accurate and up-to date data set on the location and size of informal areas. It is thus difficult to develop mitigation strategies to reduce the risk for the local population. Here, we tackle the issue of inaccurate geodata and apply a CNN for the extraction of individual building footprints from orthophotos. With it we achieve a more reliable data base for a more precise estimation of the amount of exposed population in informal areas towards landslides