predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities.     

Simulating informal settlement growth in Dar es Salaam, Tanzania: An agent-based housing model

The simulation of the growth of informal settlements can be an essential building block to manage urbanization processes in cities of the developing world. We used agent-based modelling to develop a vector-based, micro-scale housing model to simulate the growth of informal settlements. A prototype of the housing model was implemented for Dar es Salaam, Tanzania. The results show that such a vector-based housing model built on three simple rules of spatial change (infilling, extension and enlargement of existing houses) can successfully simulate the housing pattern of informal settlements growth.     

The emergence of slums: A contemporary view on simulation models

The existence of slums or informal settlements is common to most cities of developing countries. Its role as single housing delivery mechanism has seriously challenged the popular notion held by policy makers, planners and architects. Today informality is a paradigm of city making and economic growth in Africa, Asia and Latin America. This paper discusses the role of computer simulation models to understand the emergence and growth of slums in developing countries. We have identified the key factors influencing the growth of slums and formulated a standardized set of criteria for evaluating slum models. The review of existing computer simulation models designed to understand slum formation and expansion enabled us to define model requirements and to identify new research questions with respect to exploring the dynamics of slums.     

Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night

Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.     

Breaking ground: Automated disturbance detection with Landsat time series captures rapid refugee settlement establishment and growth in North Uganda

Since 2015, Uganda has welcomed over 700,000 refugees from South Sudan, Democratic Republic of the Congo, Burundi, and other East African nations, and currently hosts over 1.4 million refugees with 92% of that population living in UNHCR-managed settlements. Despite refugee settlements being essential spaces for physical protection and humanitarian aid distribution and reception, the sheer rate of refugee influx and settlement growth has introduced uncertainties around site planning, aid delivery, food security, and landscape change. For example, there is little publicly available information on settlement establishment, growth, or changes in land use/land cover for the vast majority of UNHCR-managed settlements in Uganda and around the world. To address this shortcoming, this research characterizes the spatial and temporal patterns of refugee settlement landscape dynamics using the case study of the Pagirinya Refugee Settlement in Northern Uganda, Landsat and Sentinel-2 satellite image time series, and BFAST, an automated land cover disturbance detection algorithm. To delineate the extent of the settlement and surrounding disturbance, a refugee settlement boundary was generated using a 2018 Landsat NDVI composite, which included land disturbed by settlement establishment and subsequent growth. Landsat time series data were sampled within this boundary to parametrize a BFAST model to detect settlement disturbance, which was deployed over 351 Landsat images from 2005 to 2018. This approach yielded sub-monthly land cover disturbances from 2016 to 2017 with an accuracy of 87.5% resulting from the rapid (within one month) settlement establishment, road construction, the spread of dwellings and other built-up infrastructure throughout the settlement, as well as the conversion of natural grassland to small-scale agriculture within the first six months after refugee settlement began. These results were generated using open-access data and open-source algorithms to pave the way for developing a near real-time satellite image-based settlement monitoring framework, which would aid refugee response and evaluation efforts that are central to Uganda's refugee hosting and settlement plans, as well as implementation of the Global Compact on Refugees.     

Recent publications

Abstract

Although the idea of using a satellite as a source of data for settlement studies is not new, actual measurements of the areal extent of settlements on a large scale has been meagre. A digital analytical approach has been used to identify and measure the areal extent of settlements in the Oyo-Ogbomoso-Ilorin area of Nigeria. Using 95 sampled pixels selected from Ilorin, Ogbomoso and Oyo as training areas, an area of about 249 km² has been classified as settlements. It was observed that settlements with built-up areas of about 300 h were identifiable from Landsat. Despite this observation, there is a need for further research to determine the level of accuracy of settlement identification and areal measurements which is possible from Landsat MSS data. The procedure of selecting subscenes to define known settlements is suggested as being a more accurate way of measuring the areal extent of settlements.     

A visual analytics framework for spatio-temporal analysis and modelling

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis.     

Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area

Urbanization process is a major factor of change in the Mediterranean region where pre-urban cities and new urban settlements have raised over the past decades. Several cities rapidly became regional centres or international nodes according to economic and political pressures. Urbanization (and informal settlement) causes land cover changes which can lead to deeper social, economic and environmental changes. The main objective of this paper is to provide time-series information to define and locate the evolution trends of the Tunis Metropolitan Area. In a first step, satellite imagery has been used (1986-1996 SPOT XS) to extract the land cover, identify the urbanization processes and estimate the changes. One of the main aspects is to locate informal settlement areas that grow significantly along the roadway networks in the Tunisian capital. Results show a global progression of the built-up areas of 13% in 10 years. In a second step, the urban growth evolution has been approached by using a potential model that provides general trends of feasible urban expansion, taking into account protection laws of natural and agricultural land. This type of model has not been tested on developing cities and as such it corresponds to a new planning contribution for planners who have no concept of spatially how their urban areas have changed over time and where the growth is occurring. In this case, it has been calibrated over the period of 1986-1996, and then applied to predict the location of the built-up growth over the next 10 years (1996-2006). These results can provide local authorities and other stakeholders with information towards decision support documents for future planning and monitoring plans. Moreover, they can be updated systematically through the integration of remote sensing data.     

Regional mapping of human settlements in southeastern China with multisensor remotely sensed data

Mapping human settlements from remotely sensed data at regional and global scales has attracted increasingly attention but remains a challenge. The thresholding technique is a common approach for settlement mapping based on the DMSP-OLS data. However, this approach often omits the areas with small proportional settlements such as towns and villages and overestimates urban extents, resulting in information loss of spatial patterns. This paper explored an integrated approach based on a combined use of multiple remotely sensed data to map settlements in southeastern China. Human settlements for selected sites were mapped from Landsat ETM+ images with a hybrid approach and they were used as reference data. The DMSP-OLS and Terra MODIS NDVI data were combined to develop a settlement index image. This index image was used to map a pixel-based settlement image with expert rules. A regression model was established to estimate fractional settlements at the regional scale, which the DMSP-OLS and MODIS NDVI data were used as independent variables and the settlement data derived from ETM+ images were used as a dependent variable. This research indicated that a combination of DMSP-OLS and NDVI variables provided a better estimation performance than single DMSP-OLS or NDVI variable, and the integrated approach for settlement mapping at the regional scale was promising. Compared to the results from the traditional thresholding technique, the estimated fractional settlement image in this paper greatly improved the spatial patterns of settlement distribution and accuracy of settlement areas. This paper provided a rapid and accurate approach to estimate fractional settlements from coarse spatial resolution images at the regional scale by combining a limited number of medium spatial resolution images. This research is especially valuable for timely updating settlement databases at regional and global scales with limited time, labor, and cost.     

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

     

An ontology of slums for image-based classification

Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.     

iCity: A GIS–CA modelling tool for urban planning and decision making

The objective of this study is to present a novel tool for predictive modelling of urban growth. The proposed tool, named iCity – Irregular City, extends the traditional formalization of cellular automata (CA) to include an irregular spatial structure, asynchronous urban growth, and a high spatio-temporal resolution to aid in spatial decision making for urban planning. The iCity software tool was developed as an embedded model within a common desktop geographic information system (GIS) with a user-friendly interface to control modelling operations for urban land-use change. This approach allows the model developer to focus on implementing model logic rather than developing an entire stand-alone modelling application. It also provides the model user with a familiar environment in which to run the model to simulate urban growth.     

Satellite-based detection of bamboo expansion over the past 30 years in Mount Tianmushan,China

The present work aims to detect bamboo expansion and its impact on carbon storage in a thick forest in the most recent 30 years. The research area is the national nature reserve of Tianmushan, Zhejiang Province, China, and the present paper monitored bamboo expansion from 1984 to 2015. Multi-spectral band and vegetation indices from Landsat images in summer and winter are used combined to improve the accuracy of detection using a support vector machine (SVM) classifier. Expansion of bamboo over this period is evident. Total expansion is 161%, the fastest annual rate being 11.6%. However, over recent decades the growth of bamboo has been inhibited by human activity and the total area has decreased by 21%. Evergreen broadleaf forest is the most vulnerable to invasion by bamboo at a ratio of about 65%, and this expanding trend has been brought under effective control. Carbon storage was estimated using sample plot surveys and modelling based on key ecological forests. According to our estimation using carbon storage models, the total carbon storage of Tianmushan has declined by circa 4.7% due to bamboo expansion in the past three decades.     

A Kohonen Self-Organizing Map Approach to Modeling Growth Pole Dynamics

Economic growth poles remain an important concept in regional development policy analysis. Recent attention has focused for example on the emergence and promotion of technopoles, or agglomerations of high technology activities within a region. This paper presents a neural network approach for analyzing these evolutionary processes. Kohonen self-organizing maps are used to simulate the spread and backwash effects associated with the emergence of regional growth poles. Unlike other models of growth pole development, which rely heavily on conventional economic theory, the approach outlined in this paper allows for increasing returns, multiple equilibria, `satisficing' behavior, imperfect information and stochasticity. The model could be used to explore spatio-temporal dynamics under different assumptions relating to industrial composition, interindustry linkages, and the spatial juxtaposition of firms (industries). The model is applied to the Washington, D.C. metropolitan area, and the patterns of development that have arisen from the location behavior of high technology firms in this region.     

Monitoring and assessing reclamation settlement in coastal areas with advanced InSAR techniques: Macao city (China) case study

In the present study, we have investigated spatial-temporal behaviours of the land subsidence induced by reclamation activities in Macao Special Administrative Region, a coastal city of southern China. An advanced Synthetic Aperture Radar Interferometry (InSAR) technique, referred to as Interferometric Point Target Analysis (IPTA), was applied to retrieve the deformation rate and displacement time series during the period from July 2006 to March 2009. Validated by levelling survey measurements, the InSAR-derived results showed a fairly stable and homogeneous pattern within the land of Macao before 1912, which consists mainly of the three granitic islands of Macao Peninsula, Taipa Island and Coloane Island. In contrast, relatively large deformation rates (between???15 and???41 mm year^???1) and local spatial settlement variability were discovered within the latest reclamation areas. A quantitative comparison analysis of the relationship between the observed settlements and the evolution of land reclamation indicated a time-dependent settlement behaviour with respect to the age of the reclamation. Another key result was that differential settlements were detected over short distances in reclamation areas, particularly between the ground surfaces and the adjacent buildings, thus providing valuable information not only for early detection and remedial activities of potential settlement of such buildings but also for the design of future facilities adjacent to the buildings, particularly for that of large infrastructure developments.     

Spatial scaling of stable night lights

City size distributions, defined on the basis of population, are often described by power laws. Zipf's Law states that the exponent of the power law for rank-size distributions of cities is near −1. Verification of power law scaling for city size distributions at continental and global scales is complicated by small sample sizes, inappropriate estimation techniques, inconsistent definitions of urban extent and variations in the accuracy and spatial resolution of census administrative units. We attempt to circumvent some of these complications by using a continuous spatial proxy for anthropogenic development and treat it as a spatial complement to population distribution. We quantify the linearity and exponent of the rank-size distribution of spatially contiguous patches of stable night light over a range of brightnesses corresponding to different intensities of development. Temporally stable night lights, as measured by the Defense Meteorological Satellite Program-Operational Line Scanner (DMSP-OLS), provide a unique proxy for anthropogenic development. Brightness and spatial extent of emitted light are correlated to population density (Sutton et al., 2001), built area density (Elvidge et al., 2007c) and economic activity ( [Doll et al., 2006] and [Henderson et al., 2009]) at global scales and within specific countries. Using a variable brightness threshold to derive spatial extent of developed land area eliminates the complication of administrative definitions of urban extent and makes it possible to test Zipf's Law in the spatial dimension for a wide range of anthropogenic development. Higher brightness thresholds generally correspond to more intense development while lower thresholds extend the lighted area to include smaller settlements and less intensively developed peri-urban and agricultural areas. Using both Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE) to estimate power law linearity and exponent of the resulting rank-size distributions across a range of upper tail cutoffs, we consistently find statistically significant exponents in the range −0.95 to −1.11 with an abrupt transition to very large, extensively connected, spatial networks of development near the low light detection limit of the sensor. This range of exponents and transition are observed at both continental and global scales. The results suggest that Zipf's Law also holds for spatial extent of anthropogenic development across a range of intensities at both continental and global scales. The implication is that the dynamics of urban growth and development may be represented as spatial phase transitions when the spatial extent and intensity of development are treated as continuous variables rather than discrete entities.     

Spatio-temporal predicates

Investigates temporal changes of topological relationships and thereby integrates two important research areas: first, 2D topological relationships that have been investigated quite intensively, and second, the change of spatial information over time. We investigate spatio-temporal predicates, which describe developments of well-known spatial topological relationships. A framework is developed in which spatio-temporal predicates can be obtained by temporal aggregation of elementary spatial predicates and sequential composition. We compare our framework with two other possible approaches: one is based on the observation that spatio-temporal objects correspond to 3D spatial objects for which existing topological predicates can be exploited. The other approach is to consider possible transitions between spatial configurations. These considerations help to identify a canonical set of spatio-temporal predicates     

Geospatial modelling of urban growth for sustainable development in the Niger Delta Region,Nigeria

Urban land expansion usually alters land-cover status, which may indirectly affect the physical environment. Understanding the extent of its effects is usually estimated using geospatial techniques. In this study, urban landscape’s changes between 1985 and 2015 were analysed through some selected spatial metrics in Niger Delta, Nigeria. In addition, we incorporated sustainable urban development goals in projecting the future urban expansion to observe their impact in this rapidly urbanizing region with a total area of 109,281.30 km². A Cellular Automata/Markov Chain (CA-Markov) model was used to project the year 2030 land cover of the region using two scenarios: by considering existing settlements as the only constraints and by combining them with some selected United Nations Sustainable Development Goals (UN SDGs) constraints (forest reserves, population, and economy). Comparing the two scenarios, the projected land-cover changes under the first scenario resulted in net loss and gains of ?7.37%, 11.84%, and 50.88%, while the second scenario produced net loss and gains of ?4.72%, 7.43%, and 48.37% in forest, farmland, and built-up area between 2015 and 2030, respectively. The difference between the two scenarios showed that strict adherence to the UN SDGs will reduce tropical deforestation in Niger Delta or similar region.     

基于GIS的人居环境气候舒适度评价——以河南省为例

人居环境科学是一门以包括乡村、集镇、城市等在内的所有人类聚居形式为研究对象的科学,它的研究内容非常广泛,涉及政治、社会、文化、技术等各个方面。而在人居环境评价的各项指标中,气候因子是最为重要、最为基础的因子之一。通过对人居环境的定义及评价指标的分析,着重研究利用地理信息系统进行气候舒适度评价的方法,以便为人居环境的综合评价提供基础依据。     

An integrated spatio-temporal classification method for urban fringe change detection analysis

The urban fringe is the transition zone between urban land use and rural land use. It represents the most active part of the urban expansion process. Change detection using multi-temporal imagery is proven to be an efficient way to monitor land-use/land-cover change caused by urban expansion. In this study, we propose a new multi-temporal classification method for change detection in the urban fringe area. The proposed method extracts and integrates spatio-temporal contextual information into multi-temporal image classification. The spatial information is extracted by object-oriented image segmentation. The temporal information is modelled with temporal trajectory analysis with a two-step calibration. A probabilistic schema that employs a global membership function is then used to integrate the spectral, spatial and temporal information. A trajectory accuracy measurement is proposed to assist the comparison on the performances of the integrated spatio-temporal method and classical pixel- and ‘snapshot’-based classification methods. The experiment shows that the proposed method can significantly improve the accuracies of both single scene classification and temporal trajectory analysis.