Hasil untuk "Settlements"

K. Lynch

C. Linard, M. Gilbert, R. Snow et al.

The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.

E. Gosling

M. Marconcini, A. Metz-Marconcini, Soner Üreyen et al.

Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10 m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpretation of very high resolution Google Earth imagery and outperforms all other similar existing layers; in particular, it considerably improves the detection of very small settlements in rural regions and better outlines scattered suburban areas. The dataset can be used at any scale of observation in support to all applications requiring detailed and accurate information on human presence (e.g., socioeconomic development, population distribution, risks assessment, etc.). Measurement(s) global settlement extent Technology Type(s) satellite imaging • machine learning Factor Type(s) geographic location Sample Characteristic - Environment anthropogenic environment • populated place Sample Characteristic - Location Earth (planet) Measurement(s) global settlement extent Technology Type(s) satellite imaging • machine learning Factor Type(s) geographic location Sample Characteristic - Environment anthropogenic environment • populated place Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12424970

W. J. Dixon

Colin Mcfarlane

Andrew Renninger

Brasília offers a rare test of how urban form shapes experienced segregation. Built almost at once around modernist neighbourhood units, then expanded through planned satellites and informal peripheries, it lets us ask whether urban form turns mobility into mixing or into a more efficient engine of separation. We combine data on human mobility with urban morphometrics, amenities, road networks, along with enclosures and tessellations that capture segregation at the scales where access is structured: districts, neighbourhoods, blocks, and street-and-building cells. We find that segregation intensifies as resolution sharpens, from 0.282 at the district scale to 0.545 at the block scale, indicating that Brasília looks most integrated at coarse units and most segregated where everyday encounters are actually organised. Mobility softens home segregation for most users, but not symmetrically: poorer groups travel farther, while affluent groups remain the most selectively exposed. civic cores and mid-rise, mixed-use areas are the least segregated morphotypes, yet they occupy only a sliver of the metropolis. Elsewhere, rich lakefront suburbs and dense poor settlements reach similarly high segregation through opposite spatial logics. Amenities predict lower segregation, while barriers and enclosed residential interiors predict higher segregation. Built form explains more of this pattern than visit volume alone in the segregation models: integration is less a property of residential design than of shared destinations and porous connections. Planned capitals can build order without building isolation if they distribute mixing space rather than sequestering it.

C. Small, F. Pozzi, C. Elvidge

Ren-peng Chen, Pin Zhang, X. Kang et al.

Abstract In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge.

Arlen F. Chase, Diane Z. Chase, J. Weishampel et al.

Xue Xia, Randall Balestriero, Tao Zhang et al.

Historical maps are unique and valuable archives that document geographic features across different time periods. However, automated analysis of historical map images remains a significant challenge due to their wide stylistic variability and the scarcity of annotated training data. Constructing linked spatio-temporal datasets from historical map time series is even more time-consuming and labor-intensive, as it requires synthesizing information from multiple maps. Such datasets are essential for applications such as dating buildings, analyzing the development of road networks and settlements, studying environmental changes etc. We present MapSAM2, a unified framework for automatically segmenting both historical map images and time series. Built on a visual foundation model, MapSAM2 adapts to diverse segmentation tasks with few-shot fine-tuning. Our key innovation is to treat both historical map images and time series as videos. For images, we process a set of tiles as a video, enabling the memory attention mechanism to incorporate contextual cues from similar tiles, leading to improved geometric accuracy, particularly for areal features. For time series, we introduce the annotated Siegfried Building Time Series Dataset and, to reduce annotation costs, propose generating pseudo time series from single-year maps by simulating common temporal transformations. Experimental results show that MapSAM2 learns temporal associations effectively and can accurately segment and link buildings in time series under limited supervision or using pseudo videos. We will release both our dataset and code to support future research.

Luca Vaccino, Alana K. Lund, Shirley J. Dyke et al.

Establishing long-term human settlements in deep space presents significant challenges. Harsh environmental conditions, such as extreme temperature fluctuations, micrometeorite impacts, seismic activity, and exposure to solar and cosmic radiation pose obstacles to the design and operation of habitat systems. Prolonged mission duration and the vast distances from Earth introduce further complications in the form of delayed communication and limited resources, making autonomy especially desirable. Enabling simulation of the consequences of disruptions and their propagation through the various habitat subsystems is important for the development of autonomous and resilient space habitats. While existing simulation tools can assist in modeling some of these aspects, the integration of damage propagation, detection and repair in a computational model is rarely considered. This paper introduces and demonstrates a simulation architecture designed to model these aspects efficiently. By combining physics-based and phenomenological models, our approach balances computational efficiency with model fidelity. Furthermore, by coordinating subsystems operating at different time scales, we achieve real-time simulation capabilities. After describing the architecture, we demonstrate its application within HabSim, a space habitat system model developed by the NASA-funded Resilient Extraterrestrial Habitat Institute (RETHi). In these scenarios we consider fire hazard propagation within a lunar habitat to illustrate both how our architecture supports the modeling of disruption propagation, detection, and repair in a simulation environment and how the HabSim model can be leveraged for through stochastic simulations to support resilience assessment. The architecture developed herein is efficient and scalable, enabling researchers to gain insight into resilience, autonomy and decision-making.

Pin Zhang, Ren-peng Chen, Huai-na Wu

Abstract Settlement control is an essential part of the tunnel construction process. This paper proposes two novel computational models based on the Random Forest (RF) algorithm for supporting automatically steering Earth Pressure Balanced (EPB) shield. The first model is utilized for predicting tunneling-induced settlement and the other estimates shield operational parameters. A PSO-RF hybrid algorithm which intergrates the Particle Swarm Optimization (PSO) and the RF algorithms is proposed to optimize shield operational parameters when the settlement exceeds the tolerated value. The proposed models are adopted in the case study of Changsha Metro Line 4 project. The results indicate that the predicted settlements show great agreement with the measured settlements. The face pressure and grout filling are the most significant shield operational parameters to control the settlement as a result of Global Sensitivity Analysis (GSA). The anomalous settlement (≥10 mm) can be controlled under tolerated value after the face pressure and grout filling values are optimized by the PSO-RF hybrid algorithm. Simultaneously, the consistency of the face pressure and grout filling values calculated by the PSO-RF and the grid search method demonstrates the feasibility and applicability of proposed hybrid algorithm.

Benjamin M. Marx, Thomas M. Stoker, T. Suri

G. Ikenberry, S. Stedman, D. Rothchild et al.

Linda Babcock, G. Loewenstein, S. Issacharoff et al.

Xiaotong Zhang, Linjun Yu, Yang Li

In community planning, due to the lack of evidence regarding the selection of media tools, this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process. First, this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning. Second, the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions: acceptability, cost-effectiveness, and applicability. Third, strategies for applying media tools in the four phases of communicative planning—namely, state analysis, problem identification, contradictory solution and optimization—are described. Finally, trends in the development of media tools for community planning are explored in terms of multistakeholder engagement, supporting scientific decision-making and multiple-type media integration. The results provide a reference for developing more inclusive, effective, and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Jie Han, M. Gabr

H. Taubenböck, M. Weigand, T. Esch et al.

Abstract With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with >300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.

Johannes H. Uhl, Stefan Leyk

Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m x 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, in both rural and urban areas, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.