Modelling Day and Night-Time Population using a 3D Urban Model

Dasymetric methods are commonly used to redistribute or disaggregate (census) population data, using either simple binary or multi-layer models. Most models show limitations in high density built-up areas as they commonly ignore the 3D dimension (meaning buildings height) of multi-story urban environments. For example, simple dasymetric models only allocate the population counts to built-up areas, without considering differences between areas of multi-story and single-story buildings. Furthermore, such models only allow the disaggregation of ‘night-time’ population data, while for many urban applications such as transport, health or hazard, the location of ‘day-time’ population is of interest. This research presents an initial approach to model day and night-time population using as case study an Indian city (Kalyan-Dombivli). For most Indian cities, census population data is only available for wards, while day-time population data is either not available or of very poor quality. Besides census data and ancillary spatial data, this research uses a 3D urban model, extracted from Cartosat stereo-images. First, the extracted height from the stereo-image is used in combination with building footprints to disaggregate census population data at wards to ‘night-time’ population per building. Second, a classification of economically active areas is constructed based on the 3D urban model in combination with other spatial layers (e.g. transport layers) to model the day-time population. The result shows different concentration of population during day and night-time across ward boundaries as well as it confirms the potential of 3D data to disaggregate population data

A Global Estimate of the Size and Location of Informal Settlements

Slums are a structural feature of urbanization, and shifting urbanization trends underline their significance for the cities of tomorrow. Despite their importance, data and knowledge on slums are very limited. In consideration of the current data landscape, it is not possible to answer one of the most essential questions: Where are slums located? The goal of this study is to provide a more nuanced understanding of the geography of slums and their growth trajectories. The methods rely on the combination of different datasets (city-level slum maps, world cities, global human settlements layer, Atlas of Informality). Slum data from city-level maps form the backbone of this research and are made compatible by differentiating between the municipal area, the urbanized area, and the area beyond. This study quantifies the location of slums in 30 cities, and our findings show that only half of all slums are located within the administrative borders of cities. Spatial growth has also shifted outwards. However, this phenomenon is very different in different regions of the world; the municipality captures less than half of all slums in Africa and the Middle East but almost two-thirds of all slums in cities of South Asia. These insights are used to estimate land requirements within the Sustainable Development Goals time frame. In 2015, almost one billion slum residents occupied a land area as large as twice the size of the country of Portugal. The estimated 380 million residents to be added up to 2030 will need land equivalent to the size of the country of Egypt. This land will be added to cities mainly outside their administrative borders. Insights are provided on how this land demand differs within cities and between world regions. Such novel insights are highly relevant to the policy actions needed to achieve Target 11.1 of the Sustainable Development Goals (“by 2030, ensure access for all to adequate, safe and affordable housing and basic services, and upgrade slums”) as interventions targeted at slums or informal settlements are strongly linked to political and administrative boundaries. More research is needed to draw attention to the urban expansion of cities and the role of slums and informal settlements

Analysing sub-standard areas using high resolution remote (VHR) sensing imagery

Urban planners and managers in developing countries often lack information on sub-standard areas. Base data mostly refer to relatively large and heterogeneous areas such as census or administrative wards, which are not necessarily a relevant geographical unit for representing and analysing deprivations. Moreover sub-standard areas are diverse, ranging from unrecognized slum areas (often in the proximity of hazardous areas) to regularized areas with poor basic services, and information on this diversity is difficult to capture. Sub-standard areas in Indian cities are typical examples of that diversity. In Mumbai, sub-standard areas range from unrecognized slum pockets to large regularized sub-standard areas. This paper explores the usage of the latest generation of very high (spatial and spectral) resolution satellite images using 8-Band images of WorldView-2 to analyse spatial characteristics of sub-standard areas. The research illustrates how VHR imagery helps in rapidly extracting spatial information on sub-standard areas as well as provides a better understanding of their morphological characteristics (e.g. built-up density, greenness and shape). For this study an East-West cross-section of Mumbai (India) was selected, which is strongly dominated by a variety of sub-standard areas. The research employed image segmentation to extract building footprints and used texture and spatial metrics to analyse physical characteristics of sub-standard areas, combined with purposely-collected ground-truth information. The results show the capacity of this methodology for characterizing the diversity of sub-standard areas in Mumbai, providing strategic information for urban management

Evaluating the Ability to Use Contextual Features to Map Deprived Areas 'Slums' in Multiple Cities

Population living in deprived conditions continues to grow, highlighting the urgent need for accurate high-resolution maps and detailed statistics to plan interventions and monitor changes. Unfortunately, data on deprived areas or "slums"is often unavailable, incomplete, or outdated. Leveraging satellite imagery can offer timely, and consistent information on deprived areas over large area However, there are limited studies that use free and open source data that can be used to map deprived areas over large areas and across multiple cities. To address these challenges, this study examines a scalable and transferable modeling approach to map deprived areas using contextual features extracted from freely available Sentinel-2 data. Models were trained and tested on three Sub-Sahara cities: Lagos Nigeria, Accra Ghana, and Nairobi, Kenya. The results indicate that models in individual city achieved F1 scores from 0.78-0.95 for the three cities. Additionally, the results indicate that the proposed approach may allow for the ability to transfer models from city to city allowing for large area and across city mapping.</p

Making the third dimension (3D) explicit in hedonic price modelling : A case study of Xi’an, China

Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R2 (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R2 (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation. © 2020 by the authors. Li-censee MDPI, Basel, Switzerland

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups