International journal of applied earth observation and geoinformation : ITC journal最新文献_第2页

Uncovering the drivers of gender inequality in perceptions of safety: An interdisciplinary approach combining street view imagery, socio-economic data and spatial statistical modelling 揭示安全感方面性别不平等的驱动因素：结合街景图像、社会经济数据和空间统计建模的跨学科方法

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-24 DOI: 10.1016/j.jag.2024.104230

The perception of safety significantly impacts residents’ urban living and socio-economic development. However, the phenomenon and drivers of gender differences in safety perceptions have not received sufficient emphasis, resulting in the gradual exacerbation of gender inequality in urban environments. To address this issue, we explored a research methodology that integrates visual perception with socio-environmental characteristics to more comprehensively explain gender differences in safety perceptions. We conducted an empirical investigation in the primary urban area of Nanjing, China. We explored the spatial distribution characteristics of safety perception differences using the Gradient Boosting Decision Tree model and spatial autocorrelation analysis. Additionally, we examined the impact of visual elements on gender differences through ridge regression analysis. Given the unsteady spatial distribution of urban environmental data and safety perceptions, we employed multi-scale geographically weighted regression models to account for differential distributions. These models captured the spatial relationships between indicators of socio-economic characteristics, urban environmental characteristics, social media vitality, and safety perceptions. Some interesting findings were identified in the study: (1) Gender differences were concentrated in high-density old urban areas and expansive agricultural land. (2) Women have more negative perceptions of the color richness of streets and the enclosure of interfaces. (3) Characteristics of local people’s activities positively influenced perceptions of safety, whereas characteristics representing diverse people’s activities more negatively characterized perceptions of safety for men. This study contributes a comprehensive and replicable methodology to the research on gender differences in urban perceptions, offering insights for urban planning decisions and promoting gender inclusivity.

安全感极大地影响着居民的城市生活和社会经济发展。然而，安全感知中的性别差异现象和驱动因素并未得到足够重视，导致城市环境中的性别不平等现象逐渐加剧。针对这一问题，我们探索了一种将视觉感知与社会环境特征相结合的研究方法，以更全面地解释安全感知中的性别差异。我们在中国南京的主城区进行了实证调查。我们利用梯度提升决策树模型和空间自相关分析，探讨了安全感差异的空间分布特征。此外，我们还通过脊回归分析研究了视觉元素对性别差异的影响。鉴于城市环境数据和安全感的空间分布不稳定，我们采用了多尺度地理加权回归模型来考虑差异分布。这些模型捕捉了社会经济特征指标、城市环境特征、社交媒体活力和安全感之间的空间关系。研究中发现了一些有趣的发现：（1）性别差异主要集中在高密度的老城区和广阔的农业用地。(2）女性对街道色彩的丰富性和界面的封闭性有更多的负面感知。(3) 当地人的活动特征对安全感有积极影响，而代表不同人群活动的特征对男性的安全感有更消极的影响。这项研究为城市认知中的性别差异研究提供了一种全面的、可复制的方法，为城市规划决策和促进性别包容提供了启示。

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引用次数: 0

A satellite-derived bathymetry method combining depth invariant index and adaptive logarithmic ratio: A case study in the Xisha Islands without in-situ measurements 结合深度不变指数和自适应对数比率的卫星水深测量方法：西沙群岛无现场测量的案例研究

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-23 DOI: 10.1016/j.jag.2024.104232

Accurate bathymetric data is crucial for various aspects such as marine resource exploitation and marine ecological conservation. Currently, satellite-derived bathymetry (SDB) based on empirical and physical models has been widely utilized in constructing underwater terrain in shallow seas. However, the application of such SDB models is limited in remote island reef areas lacking in-situ measurement data. To overcome this issue, the manuscript proposes an unconstrained SDB optimization method without in-situ measurement data, utilizing satellite multispectral imagery (Geoeye-1) and spaceborne LiDAR data (ICESat-2). By classifying the seafloor substrate in coral reef areas into sandy and coral, based on the depth invariant index (DII), we employ an adaptive logarithmic ratio model for unconstrained SDB. The ICESat-2 LiDAR data are then used to correct the SDB results, achieving bathymetry optimization in the coral reef area of the Xisha Islands. Additionally, the proposed method is applied to Yuanzhi Island of the Xisha Islands, and the accuracy of the bathymetric results is evaluated against ALB (Airborne LiDAR Bathymetry) data. The findings demonstrate that compared to conventional methods, our method can improve the accuracy of SDB results with good adaptability. In the Yuanzhi Island area, the proposed method yields SDB results with an R² of 0.93, an MAE (Mean Absolute Error) of 0.94, and an RMSE (Root Mean Square Error) of 1.12 m, compared to ALB data. The average error is less than 10 % of the maximum depth, essentially meeting the requirements of the International Hydrographic Organization (IHO) standards for depth measurement error when depth is <20 m. This study can offer a novel approach for enhancing bathymetric accuracy around offshore and remote islands, where gathering underwater terrain data is challenging.

准确的测深数据对海洋资源开发和海洋生态保护等各个方面都至关重要。目前，基于经验和物理模型的卫星水深测量（SDB）已被广泛用于构建浅海水下地形。然而，在缺乏原位测量数据的偏远岛礁地区，此类 SDB 模型的应用受到限制。为克服这一问题，手稿提出了一种无约束 SDB 优化方法，利用卫星多光谱图像（Geoeye-1）和空间激光雷达数据（ICESat-2），无需原位测量数据。根据深度不变指数（DII）将珊瑚礁区域的海底基质分为沙质和珊瑚质，我们采用自适应对数比率模型进行无约束 SDB。然后利用 ICESat-2 LiDAR 数据修正 SDB 结果，实现了西沙群岛珊瑚礁区域的水深优化。此外，还将该方法应用于西沙群岛的远志岛，并对照 ALB（机载激光雷达测深）数据评估了测深结果的准确性。结果表明，与传统方法相比，我们的方法可以提高 SDB 结果的精度，并具有良好的适应性。在远志岛地区，与 ALB 数据相比，建议方法得到的 SDB 结果 R2 为 0.93，MAE（平均绝对误差）为 0.94，RMSE（均方根误差）为 1.12 米。平均误差小于最大深度的 10%，基本符合国际水文组织（IHO）关于深度为 20 米时深度测量误差的标准要求。这项研究为提高近海和偏远岛屿周围的测深精度提供了一种新方法，因为在这些地方收集水下地形数据具有挑战性。

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引用次数: 0

Increasing irrigation-triggered landslide activity caused by intensive farming in deserts on three continents 三大洲沙漠地区的集约化耕作导致灌溉引发的滑坡活动不断增加

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-23 DOI: 10.1016/j.jag.2024.104242

Population growth and agricultural intensification lead to stress on landscapes that are highly sensitive to land-use changes. An increase in irrigation-triggered landslides (ITL) in dry climates has negative impacts on local communities. However, evolution and global impacts of ITL are little-known. Here, we use Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR), vectorization, and differential method to study surface deformation, ground displacement, and changes in headscarp morphology and topography in regions prone to ITL, aiming to uncover the evolution and spatiotemporal distribution of ITL. Findings show that the most severe surface deformation of ITL occurs on the landslide body. Meanwhile, the ITL displacement curve indicates the ITL will maintain continuous movement for at least 7 years, while ancient ITL also poses a threat. Moreover, the headscarp of ITL shows lateral expansion and longitudinal retrogression on the horizontal ground, whereby the scale of expansion is greater than that of retrogression, which transforms landslides into landslide clusters. Finally, the topographic changes further reveal that the main development pattern of ITL is lateral expansion. We suggest that the frequency and disaster-causing ability of ITL will increase greatly with further population growth and related intensification in the agricultural sector.

人口增长和农业集约化导致对土地使用变化高度敏感的地貌受到压力。在干旱气候条件下，灌溉引发的山体滑坡（ITL）的增加对当地社区产生了负面影响。然而，人们对 ITL 的演变和全球影响知之甚少。在此，我们利用多时相干涉合成孔径雷达（MT-InSAR）、矢量化和差分法研究了易发生 ITL 地区的地表形变、地面位移、头痕形态和地形的变化，旨在揭示 ITL 的演变和时空分布。研究结果表明，国际滑坡体的表面变形最为严重。同时，国际滑坡体位移曲线表明，国际滑坡体将至少持续运动 7 年，而古老的国际滑坡体也构成了威胁。此外，在水平地面上，国际滑坡体的头嵴表现为横向扩展和纵向后退，其中扩展的规模大于后退的规模，从而使滑坡体转变为滑坡群。最后，地形变化进一步揭示了国际滑坡的主要发展模式是横向扩展。我们认为，随着人口的进一步增长和农业部门的相关集约化发展，国际滑坡的发生频率和致灾能力将大大提高。

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引用次数: 0

SLRCNN: Integrating sparse and low-rank with a CNN denoiser for hyperspectral and multispectral image fusion SLRCNN：将稀疏和低秩与 CNN 去噪器相结合，用于高光谱和多光谱图像融合

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-23 DOI: 10.1016/j.jag.2024.104227

Fusion of hyperspectral image (HSI) and multispectral image (MSI) is a prevalent scheme to generate a HSI with enhanced spatial resolution. Current methods often fail to sufficiently leverage the effective spectral and spatial priors existing in the observed HSI and MSI to further enhance the fusion performance. To address this limitation, this paper proposes a novel HSI-MSI fusion approach, which integrates Sparse and Low Rank with a CNN denoiser (SLRCNN) while considering spectral dictionary optimization. Firstly, an initialized spectral dictionary is derived from the HSI. Next, the spatial coefficients optimization model is established by incorporating the sparse prior, local low-rank prior, and plugged image prior simultaneously, where the

l_{1}

norm is imposed to promote the sparse prior, and the super-pixel segmentation strategy is conducted on the MSI to impose the local low-rank prior while a well-trained CNN denoiser is plugged in to enforce the image prior. Then, the spectral dictionary optimization model is constructed to refine the initial spectral dictionary, capturing more detailed spectral characteristics to further improve the fusion results. Finally, the optimization process involves applying the split-augmented Lagrangian shrinkage method and the alternating direction method of multipliers. Experimental results on simulated and real datasets, namely the Pavia University dataset, the Indian Pines dataset, and the EO-1 dataset, indicate that SLRCNN outperforms existing state-of-the-art approaches at 4x, 5x, and 6x resolutions in both qualitative and quantitative evaluation results. Specifically, the peak signal-to-noise ratio (PSNR) of SLRCNN is improved by more than 0.9 dB, 0.9 dB, and 0.2 dB while the spectral angle mapper (SAM) is decreased by more than 0.1, 0.2, and 0.2 in degree compared to other state-of-the-art methods across three datasets, respectively, which underscores the effectiveness of SLRCNN in leveraging both spatial detail reconstruction and spectral preservation.

高光谱图像（HSI）与多光谱图像（MSI）的融合是生成空间分辨率更高的 HSI 的普遍方案。目前的方法往往不能充分利用观测到的高光谱图像和多光谱图像中存在的有效光谱和空间先验来进一步提高融合性能。为解决这一局限性，本文提出了一种新颖的 HSI-MSI 融合方法，该方法将稀疏低等级与 CNN 去噪器（SLRCNN）相结合，同时考虑了光谱字典优化。首先，从 HSI 导出初始化光谱字典。接下来，同时结合稀疏先验、局部低秩先验和插入式图像先验，建立空间系数优化模型，其中施加 l1 准则以促进稀疏先验，在 MSI 上执行超像素分割策略以施加局部低秩先验，同时插入训练有素的 CNN 去噪器以执行图像先验。然后，构建光谱字典优化模型以完善初始光谱字典，捕捉更详细的光谱特征，从而进一步改善融合结果。最后，优化过程包括应用分裂增量拉格朗日收缩法和乘数交替方向法。在模拟和真实数据集（即帕维亚大学数据集、印度松树数据集和 EO-1 数据集）上的实验结果表明，SLRCNN 在 4x、5x 和 6x 分辨率下的定性和定量评估结果均优于现有的先进方法。具体来说，在三个数据集上，SLRCNN 的峰值信噪比 (PSNR) 分别提高了 0.9 dB、0.9 dB 和 0.2 dB 以上，而光谱角映射器 (SAM) 与其他先进方法相比分别降低了 0.1、0.2 和 0.2 度以上，这凸显了 SLRCNN 在利用空间细节重建和光谱保护方面的有效性。

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引用次数: 0

Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration 关中平原城市群热环境景观动因动态分析

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-22 DOI: 10.1016/j.jag.2024.104239

Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.

中国关中地区因快速城市化而导致的气候变化正成为一个日益严重的问题。以往的实证研究证实，景观格局与热环境密不可分，但基于单一时间截面图像数据的静态结果只能提供部分认识。在本文中，我们利用天气研究与预报模型（WRF）进行温度模拟，并利用 Geodetector 构建了一个动态框架，以研究影响关中平原城市群 2000-2020 年间近地面温度（NST）变化的景观因素及其相互作用。结果表明，2000-2020年关中平原城市群1月和7月平均近地面温度分别上升了0.012 ℃和0.053 ℃。从景观格局与 NST 的动态相关性来看，耕地（CPL）为负，城市用地（UBL）为正，其余景观在冬季和夏季存在差异。此外，Geodetector 的结果表明，UBL 在夏季比冬季的影响更大。这一发现有助于解释为什么夏季的 NST 平均增幅高于冬季。基于面积的景观指标的动态 Q 值（DQ）普遍大于其他空间配置指标的动态 Q 值，交互结果表明，不同土地覆被分类的景观指标得到了增强，这表明在景观规划中除了考虑面积因素外，还需要考虑景观指标之间的叠加效应。利用 WRF 从动态角度研究景观格局与热环境之间的关系，为理解和适应即将到来的气候变化提供了重要的理论参考和实践指导，有助于推动 GPUA 的可持续发展决策。

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引用次数: 0

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century 21 世纪中亚地表反照率的时空变化及其影响因素和确证路径分析

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-21 DOI: 10.1016/j.jag.2024.104233

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

地表反照率（SA）对于了解地表过程和气候模拟至关重要。本研究分析了中亚地区 2001 年至 2020 年的地表反照率变化及其影响因素，并预测了 2025 年至 2100 年的地表反照率变化。分析的因素包括积雪覆盖率、植被覆盖率、土壤湿度、平均状态气候指数（气温和降水）以及极端气候指数（热浪指数和极端降水指数）。利用皮尔逊相关系数、地理会聚交叉映射和地理检测器来量化 SA 与影响因素之间的相关性、因果关系强度和影响程度。为解决多重共线性问题，将山脊回归（RR）、地理加权山脊回归（GWRR）和片断结构方程模型（pSEM）相结合，构建了 RR-pSEM 和 GWRR-pSEM 模型。结果表明，中亚的 SA 在 2001 至 2010 年间有所增加，在 2011 至 2020 年间有所减少，预计未来还会下降。SA与各因子之间存在很强的相关性和显著的因果关系。雪盖率被认为是影响 SA 的最关键因素。与极端气候指数相比，平均气温和降水量对 SA 的影响更大，气温每升高 1 ℃，SA 就会减少 0.004。这项研究加深了人们对气候变化下 SA 变化的理解，并为分析具有多重共线性的复杂系统提供了方法框架。所提出的模型为研究地球系统科学中相互关联的因素提供了宝贵的工具。

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引用次数: 0

Evaluation of average leaf inclination angle quantified by indirect optical instruments in crop fields 评估用间接光学仪器量化的农作物叶片平均倾角

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-21 DOI: 10.1016/j.jag.2024.104206

Average leaf inclination angle (

{\bar{θ}}_{L}

) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants.

{\bar{θ}}_{L}

can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying

{\bar{θ}}_{L}

remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct

\bar{θ}

measurements (R² = 0.54, RMSE = 7.37°; R² = 0.58, RMSE = 8.08°) than DHP (R² = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the

{\bar{θ}}_{L}

quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger

{\bar{θ}}_{L}

values, and smaller gap fraction gradients led to smaller

{\bar{θ}}_{L}

values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating

{\bar{θ}}_{L}

than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating

{\bar{θ}}_{L}

. We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.

平均叶倾角（θ¯L）是一个重要的冠层结构变量，影响植物的光照制度、光合作用和蒸腾作用。θ¯L 可通过直接方法（如量角器）或间接光学仪器测量，前者耗费大量人力和时间，后者比直接方法更有效。然而，用于量化 θ¯L 的不同间接光学仪器的不确定性在很大程度上仍未量化。在这项研究中，我们评估并比较了三种主要间接光学仪器的性能：(1) LAI-2200、(2) 30°倾斜照相机和 (3) 数字半球摄影（DHP）。LAI-2200 和 30° 倾斜相机与直接θ¯测量值的一致性（R2 = 0.54，RMSE = 7.37°；R2 = 0.58，RMSE = 8.08°）高于 DHP（R2 = 0.14，RMSE = 13.96°）。间接光学仪器的不同性能可归因于间隙分数测量的准确性和 θ¯L 量化算法的性能。在使用 LAI-2200 算法时，视场天顶角的间隙分数梯度越大，θ¯L 值就越大，而间隙分数梯度越小，θ¯L 值就越小。这种误差传播在稀疏冠层中比在茂密冠层中更大。LAI-2200 算法的 Wilson G 函数在估算 θ¯L 时的表现优于 CAN_EYE 算法使用的基于椭球 LAD 函数的 G 函数。我们还提出了对 LAI-2200 算法的改进，进一步提高了 LAI-2200 和 30° 倾斜相机在估计 θ¯L 方面的性能。我们认为，低成本的 30° 倾斜相机为持续监测各种生态系统的冠层结构提供了一种前景广阔的传感器解决方案。

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引用次数: 0

Object detection in aerial images using DOTA dataset: A survey 使用 DOTA 数据集检测航空图像中的物体：一项调查

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-20 DOI: 10.1016/j.jag.2024.104208

In recent years, the Dataset for Object deTection in Aerial images (DOTA) dataset has played a pivotal role in advancing object detection in aerial images (ODAI). Despite its significance, there hasn’t been a comprehensive review summarizing its research developments. Addressing this gap, this paper offers the first comprehensive overview on the subject. Within this review, we begin by examining prevalent object detection datasets of natural scene images alongside object detection datasets of remote sensing images (RSIs). We then present an in-depth comparative analysis between these datasets and the DOTA dataset, supported by numerous charts and tables. We proceed to outline both traditional techniques for ODAI and methods rooted in deep learning. Subsequently, we provide a recap of the latest advancements in the field achieved using the DOTA dataset. Concluding our review, we delve into the current challenges facing ODAI and propose potential future research directions.

近年来，航空图像物体检测数据集（DOTA）在推进航空图像物体检测（ODAI）方面发挥了关键作用。尽管其重要性不言而喻，但一直没有对其研究进展进行全面总结。针对这一空白，本文首次对该主题进行了全面综述。在这篇综述中，我们首先研究了自然场景图像的常见物体检测数据集和遥感图像（RSI）的物体检测数据集。然后，我们通过大量图表对这些数据集和 DOTA 数据集进行了深入的比较分析。接下来，我们将概述 ODAI 的传统技术和基于深度学习的方法。随后，我们回顾了该领域利用 DOTA 数据集取得的最新进展。在回顾的最后，我们深入探讨了 ODAI 当前面临的挑战，并提出了潜在的未来研究方向。

引用次数: 0

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts 大地理数据揭示城市商业区客流动态的影响因素

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-19 DOI: 10.1016/j.jag.2024.104231

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

商业区作为城市商业和经济活动的中心，其客流在很大程度上反映了一个地区的繁荣程度。然而，以往的研究往往依赖于统计和调查数据，通常无法全面捕捉整个城市商业区的客流动态，也无法充分衡量商业区位置及其周边社区对客流的具体影响。为了弥补这些不足，本研究利用多维大地理数据资源，包括手机信号数据、兴趣点（POI）数据和交通网络数据，揭示了影响城市商业区客流的潜在因素。研究结果表明，商业区的规模、业态的多样性、停车的便利性、周边社区的工作人口和居住人口以及与城市中心的距离等因素对客流产生了重要影响。消费者偏好规模较大、位置集中、停车方便的商业区，而同质化、缺乏特色的商业业态则会降低商业区的吸引力。此外，研究还显示，商业区周围 15 分钟生活圈内的工业园区和综合体比住宅区对客流的吸引力更大。这项研究的见解不仅为新商业中心的战略布局提供了指导，也为加强城市商业空间的布局、振兴和提升现有商业区提供了一个强有力的框架。

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引用次数: 0

Simulating SAR constellations systems for rapid damage mapping in urban areas: Case study of the 2023 Turkey-Syria earthquake 模拟合成孔径雷达星座系统，用于快速绘制城市地区的破坏地图：2023 年土耳其-叙利亚地震案例研究

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2024-10-19 DOI: 10.1016/j.jag.2024.104226

This study evaluates the feasibility of using Synthetic Aperture Radar (SAR) constellations for rapid damage mapping in the aftermath of the 2023 Turkey-Syria earthquake. We specifically address the data acquisition latency challenges associated with X- and L-Band SAR constellations, including those operated by U.S. Capella Space, UMBRA Space, European ICEYE, and the Italian/Argentinian SIASGE constellation. Our analysis compares these constellations’ response times with established damage mapping techniques from open-access ESA Sentinel-1A/B and NASA NISAR missions. By integrating USGS shake maps with existing building maps, we demonstrate that the shorter revisit times and higher spatial resolutions of X-band SAR constellations can produce damage maps within hours, complementing the longer-term data provided by ESA and NASA missions. This research highlights the strengths and limitations of both approaches, emphasizing their roles in enhancing earthquake reconnaissance and damage detection efforts.

本研究评估了在 2023 年土耳其-叙利亚地震后使用合成孔径雷达 (SAR) 星群快速绘制破坏地图的可行性。我们特别讨论了与 X 波段和 L 波段合成孔径雷达星座相关的数据采集延迟挑战，包括由美国 Capella Space、UMBRA Space、欧洲 ICEYE 和意大利/阿根廷 SIASGE 星座运营的星座。我们的分析将这些星座的响应时间与开放访问的欧空局哨兵-1A/B 和美国国家航空航天局 NISAR 任务的成熟破坏测绘技术进行了比较。通过将美国地质调查局的震动地图与现有的建筑物地图进行整合，我们证明了 X 波段合成孔径雷达星座较短的重访时间和较高的空间分辨率可以在数小时内绘制出破坏地图，从而对欧空局和 NASA 任务提供的较长期数据起到补充作用。这项研究突出了这两种方法的优势和局限性，强调了它们在加强地震侦察和破坏探测工作中的作用。

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