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Yu Liu
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

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Forum
Published: 11 August 2021 in Science China Earth Sciences
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ACS Style

Yu Liu. Core or edge? Revisiting GIScience from the geography-discipline perspective. Science China Earth Sciences 2021, 1 -4.

AMA Style

Yu Liu. Core or edge? Revisiting GIScience from the geography-discipline perspective. Science China Earth Sciences. 2021; ():1-4.

Chicago/Turabian Style

Yu Liu. 2021. "Core or edge? Revisiting GIScience from the geography-discipline perspective." Science China Earth Sciences , no. : 1-4.

Journal article
Published: 12 July 2021 in Cities
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A better formalization of place - where people live, perceive, and interact with others - is crucial for understanding socioeconomic environment and human settlement. The widely used hedonic pricing model for houses was proposed from the perspective of space, focusing mostly on static house structural information and objective built environment factors. However, the value of house settlement is not only determined by its spatial settings, but also varies from one place to another with different cultures, human dynamics, human perceptions and social interactions. In this work, we introduce a place-oriented hedonic pricing model (P-HPM) that incorporates human dynamics and human perceptions of places to understand human settlement. As an empirical study, we employ a large volume of house price data in Boston and Los Angeles, including detailed house and locational amenity information. Besides, we take the hourly number of visits to places as a proxy of human mobility patterns, and obtain human perceptions of places extracted from large-scale street-view images using deep learning. The results show that the P-HPM outperformed the traditional HPM significantly in these two cities. Moreover, through a geographically weighted regression analysis and the Monte Carlo test, we find that the impacts of the proposed place-related variables on house prices are stable across space. Our results provide new insights into the assessment of human settlement values by incorporating the role of place using multi-source big geo-data.

ACS Style

Yuhao Kang; Fan Zhang; Song Gao; Wenzhe Peng; Carlo Ratti. Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling. Cities 2021, 118, 103333 .

AMA Style

Yuhao Kang, Fan Zhang, Song Gao, Wenzhe Peng, Carlo Ratti. Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling. Cities. 2021; 118 ():103333.

Chicago/Turabian Style

Yuhao Kang; Fan Zhang; Song Gao; Wenzhe Peng; Carlo Ratti. 2021. "Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling." Cities 118, no. : 103333.

Journal article
Published: 06 July 2021 in Landscape and Urban Planning
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Urban street-level greenery is empirically documented to improve mental and physical health, increase productivity, increase urban environmental equality and reduce carbon footprints. In addition, these benefits raise residents’ welfare, which has been correlated with increases in residential house prices. We measure street-level greenness in New York City through a novel Green View Index (GVI) using Google Street View images, and assess the impacts of greenness on commercial real estate prices. Using a sample of office transactions, we spatially correlate Google Street View Images for New York City over the 2010 to 2017 period. We find an 8.9% to 10.5% statistically, economically and positive transaction premium and a 5.6% to 7.8% rent premium for offices with low to high street-level greenness relative to those building transactions spatially correlated with very low greenness. Estimations are robust t with proximity to parks, subway stations, sidewalk widths, household income levels and investments by Building Improvement Districts, as well as other vital and standard office valuation features. By documenting the role of greenery in commercial building valuations, our results give a more complete understanding of the value of greenness in urban environments, as well as the economic role that urban landscape architecture, planning and development has upon cities.

ACS Style

Juncheng Yang; Helena Rong; Yuhao Kang; Fan Zhang; Andrea Chegut. The financial impact of street-level greenery on New York commercial buildings. Landscape and Urban Planning 2021, 214, 104162 .

AMA Style

Juncheng Yang, Helena Rong, Yuhao Kang, Fan Zhang, Andrea Chegut. The financial impact of street-level greenery on New York commercial buildings. Landscape and Urban Planning. 2021; 214 ():104162.

Chicago/Turabian Style

Juncheng Yang; Helena Rong; Yuhao Kang; Fan Zhang; Andrea Chegut. 2021. "The financial impact of street-level greenery on New York commercial buildings." Landscape and Urban Planning 214, no. : 104162.

Journal article
Published: 23 June 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Identifying urban functional zones is of great significance for understanding urban structure and urban planning. The rapid growth and open accessibility of multi-source big data, including remote sensing imagery and social sensing data, lead to a new way for dynamic identification of urban functional zones. Here, we propose an SOE (scene-object-economy) based learning framework which integrates the scene features from remote sensing imagery, the object features from building footprints and the economy features from POIs (points of interest). From these three perspectives, the rich information hidden in the urban zone is excavated for function identification. CNNs (convolutional neural networks) are used to extract high-level scene information from remote sensing images with different resolutions, which is distinguish from spectral and texture features of the urban zone. The object features comprising building indicators are constructed by measuring the area, perimeter, floor number, year of the building. Moreover, we extract socio-economic characteristics from POIs, which reflect the types of human activities in the urban zone. Lastly, a RF (random forest) classifier is utilized to fuse the SOE features for identifying functional zones. The SOE-based framework is applied to the case of Shenzhen city, and the accuracy reaches 90.8\% in the case of using remote sensing images with a resolution of 0.3-meter. The experimental results show that the prediction performance of SOE-based framework is significantly better than other traditional methods, and the quantitative contribution of SOE factors is also revealed in determining the functionality of urban zone.

ACS Style

Ying Feng; Zhou Huang; Yao Li Wang; Lin Wan; Yu Liu; Yi Zhang; Xv Shan. An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 7336 -7348.

AMA Style

Ying Feng, Zhou Huang, Yao Li Wang, Lin Wan, Yu Liu, Yi Zhang, Xv Shan. An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):7336-7348.

Chicago/Turabian Style

Ying Feng; Zhou Huang; Yao Li Wang; Lin Wan; Yu Liu; Yi Zhang; Xv Shan. 2021. "An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 7336-7348.

Journal article
Published: 01 June 2021 in SCIENTIA SINICA Terrae
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地理信息科学(Geographic Information Science,GIScience)是信息地理学的重要分支之一。在技术和工具层面,地理信息系统(Geographic Information System,GIS)是地理信息科学研究成果的具体实现。它在信息技术支持下,对地理空间数据进行采集、管理、分析、表达,并通过构建地理空间模拟、预测、优化等一系列方法,遵循数据、信息、知识、智慧的递进层次体系,研究揭示地理现象和要素的分布形态、相互作用、动态演化和驱动机理,从而服务于空间决策支持。地理信息系统的出现和发展,一方面得益于信息技术的发展和支持,另外,计量革命也为它提供了丰富的方法源泉(Johnston & Taylor, 1995)。地理信息系统自诞生之日,就在工具层面支持地理学研究及其应用。与此同时,人们也认识到需要探讨地理信息创建、处理、存储和使用中的科学问题。为此,Goodchild(1992)提出了地理信息科学的概念。它致力解决地理信息系统实现和应用中的基础科学问题,如美国国家地理信息与分析中心提出的三大研究主题包括:地理空间的认知模型、地理概念表达的计算方法、和信息社会的地理学(Goodchild et al. 1999)。概括而言,地理信息科学在信息系统的语境下,研究地理学基本概念和规律的抽象和形式化表达,从而为地理信息系统的实现和应用奠定理论基础(Mark 2004)。

ACS Style

Yu Liu. 地理信息科学:地理学的核心或是外缘?. SCIENTIA SINICA Terrae 2021, 1 .

AMA Style

Yu Liu. 地理信息科学:地理学的核心或是外缘?. SCIENTIA SINICA Terrae. 2021; ():1.

Chicago/Turabian Style

Yu Liu. 2021. "地理信息科学:地理学的核心或是外缘?." SCIENTIA SINICA Terrae , no. : 1.

Articles
Published: 04 May 2021 in Annals of the American Association of Geographers
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Finding one’s way is a fundamental daily activity and has been widely studied in the field of geospatial cognition. Immersive virtual reality (iVR) techniques provide new approaches for investigating wayfinding behavior and spatial knowledge acquisition. It is currently unclear, however, how wayfinding behavior and spatial knowledge acquisition in iVR differ from those in real-world environments (REs). We conducted an RE wayfinding experiment with twenty-five participants who performed a series of tasks. We then conducted an iVR experiment using the same experimental design with forty participants who completed the same tasks. Participants’ eye movements were recorded in both experiments. In addition, verbal reports and postexperiment questionnaires were collected as supplementary data. The results revealed that individuals’ wayfinding performance is largely the same between the two environments, whereas their visual attention exhibited significant differences. Participants processed visual information more efficiently in RE but searched visual information more efficiently in iVR. For spatial knowledge acquisition, participants’ distance estimation was more accurate in iVR compared with RE. Participants’ direction estimation and sketch map results were not significantly different, however. This empirical evidence regarding the ecological validity of iVR might encourage further studies of the benefits of VR techniques in geospatial cognition research.

ACS Style

Weihua Dong; Tong Qin; Tianyu Yang; Hua Liao; Bing Liu; Liqiu Meng; Yu Liu. Wayfinding Behavior and Spatial Knowledge Acquisition: Are They the Same in Virtual Reality and in Real-World Environments? Annals of the American Association of Geographers 2021, 1 -21.

AMA Style

Weihua Dong, Tong Qin, Tianyu Yang, Hua Liao, Bing Liu, Liqiu Meng, Yu Liu. Wayfinding Behavior and Spatial Knowledge Acquisition: Are They the Same in Virtual Reality and in Real-World Environments? Annals of the American Association of Geographers. 2021; ():1-21.

Chicago/Turabian Style

Weihua Dong; Tong Qin; Tianyu Yang; Hua Liao; Bing Liu; Liqiu Meng; Yu Liu. 2021. "Wayfinding Behavior and Spatial Knowledge Acquisition: Are They the Same in Virtual Reality and in Real-World Environments?" Annals of the American Association of Geographers , no. : 1-21.

Journal article
Published: 29 April 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This study marks the first attempt to cross-compare performances of popular state-of-the-art deep learning models in estimating population distribution from remote sensing images, investigate the contribution of neighboring effect, and explore the potential systematic population estimation biases. We conduct an end-to-end training of four popular deep learning architectures, i.e., VGG, ResNet, Xception, and DenseNet, by establishing a mapping between Sentinel-2 image patches and their corresponding population count from the LandScan population grid. The results reveal that DenseNet outperforms the other three models, while VGG has the worst performances in all evaluating metrics under all selected neighboring scenarios. As for the neighboring effect, contradicting existing studies, our results suggest that the increase of neighboring sizes leads to reduced population estimation performance, which is found universal for all four selected models in all evaluating metrics. In addition, there exists a notable, universal bias that all selected deep learning models tend to overestimate sparsely populated image patches and underestimate densely populated image patches, regardless of neighboring sizes. The methodological, experimental, and contextual knowledge this study provides is expected to benefit a wide range of future studies that estimate population distribution via remote sensing imagery.

ACS Style

Xiao Huang; Di Zhu; Fan Zhang; Tao Liu; Xiao Li; Lei Zou. Sensing Population Distribution from Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, and Systematic Biases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 5137 -5151.

AMA Style

Xiao Huang, Di Zhu, Fan Zhang, Tao Liu, Xiao Li, Lei Zou. Sensing Population Distribution from Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, and Systematic Biases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):5137-5151.

Chicago/Turabian Style

Xiao Huang; Di Zhu; Fan Zhang; Tao Liu; Xiao Li; Lei Zou. 2021. "Sensing Population Distribution from Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, and Systematic Biases." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 5137-5151.

Journal article
Published: 11 April 2021 in Computers, Environment and Urban Systems
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Spatio-temporal patterns of human activities can be affected by events, such as extreme weather. Events cause anomalies that could be expressed by abnormal activity patterns deviating from the inherent ones. The detection of spatio-temporal anomalies thus helps to understand the implicit influencing mechanism with which the external factors affect human activities. Existing methods of spatio-temporal anomaly detection usually treat the temporal information as attributes of spatial units, which is an over-simplification as it ignores complex temporal patterns (e.g., periodic components of time-series). Moreover, as the spatio-temporal resolutions affect expressed characteristics of anomalies, the sensitivity of anomalies to scale is also worth investigating. This study intends to detect and interpret the spatio-temporal anomalies of human activities from a multi-scale perspective. Being different from the single-scale consideration and independent consideration of multiple scales, this research investigates how the anomalies' characteristics change at multiple scales by anomaly matching. The criteria of anomaly matching are the overlapping degree of spatio-temporal influence ranges of anomalies. It helps to specify the events that caused the expressed anomalies. Besides, we introduce the time-series decomposition methods to decompose complex temporal patterns, highlighting the abnormal changes in activity patterns. The study is validated using a multi-temporal-scale simulation experiment, and a multi-spatial-scale experiment based on taxi data in Beijing. Results show that the multi-scale method can detect various anomalies. Moreover, obtained multi-scale characteristics of anomalies are easy to compare with external data, and thus benefit anomaly interpretation (validated by two sample anomalies). This study highlights the significance of scales in anomaly detection of human activities and provides references for related works.

ACS Style

Ximeng Cheng; Zhiqian Wang; Xuexi Yang; Liyan Xu; Yu Liu. Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series. Computers, Environment and Urban Systems 2021, 88, 101627 .

AMA Style

Ximeng Cheng, Zhiqian Wang, Xuexi Yang, Liyan Xu, Yu Liu. Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series. Computers, Environment and Urban Systems. 2021; 88 ():101627.

Chicago/Turabian Style

Ximeng Cheng; Zhiqian Wang; Xuexi Yang; Liyan Xu; Yu Liu. 2021. "Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series." Computers, Environment and Urban Systems 88, no. : 101627.

Journal article
Published: 18 March 2021 in Computers, Environment and Urban Systems
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Detecting borders of urban activity spaces is essential for understanding urban dynamic structures. The emerging big geo-data help to extract valuable knowledge about the relationship between urban structures and human activities at fine granularities. Despite the well-developed urban structure and transportation network design technology, barriers attenuating intra-urban travel still exist as borders of urban activity spaces. To understand the effects of activity space borders, this study first delineates the activity space borders and identifies the borders into three categories: natural, infrastructural, and administrative borders. Then, the border effect from three types of borders is evaluated through the spatial interaction model revealing their influence on intra-urban travel connections. On basis of the modeling results, we introduce an indicator, border thickness, to measure the distance increased caused by each border of activity space. This study provides a border effect perspective for investigating the urban activity spaces. We reveal the different border effects for natural, infrastructural, and administrative borders. Further, we locate the thick borders and discuss their relations with the urban structure.

ACS Style

Meihan Jin; Lunsheng Gong; Yanqin Cao; Pengcheng Zhang; Yongxi Gong; Yu Liu. Identifying borders of activity spaces and quantifying border effects on intra-urban travel through spatial interaction network. Computers, Environment and Urban Systems 2021, 87, 101625 .

AMA Style

Meihan Jin, Lunsheng Gong, Yanqin Cao, Pengcheng Zhang, Yongxi Gong, Yu Liu. Identifying borders of activity spaces and quantifying border effects on intra-urban travel through spatial interaction network. Computers, Environment and Urban Systems. 2021; 87 ():101625.

Chicago/Turabian Style

Meihan Jin; Lunsheng Gong; Yanqin Cao; Pengcheng Zhang; Yongxi Gong; Yu Liu. 2021. "Identifying borders of activity spaces and quantifying border effects on intra-urban travel through spatial interaction network." Computers, Environment and Urban Systems 87, no. : 101625.

Research article
Published: 02 March 2021 in Transactions in GIS
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In the context of rapid development, Beijing, the capital of China, is facing huge challenges in providing fair healthcare resources to residents. Although Beijing has the best healthcare resources nationwide, a highly concentrated population and uneven distribution of hospitals make the supply of medical resources tight and unbalanced. The objective of this study is to explore the healthcare resource inequality in Beijing based on spatial accessibility. The two‐step floating catchment area method was improved to measure healthcare accessibility by defining a novel distance attenuation function that conforms to the specific travel behavior of taxies to hospitals. We explored the inequality among different places and different populations. It was found that the spatial inequality of healthcare resources was evident and typical, with the dominant resources concentrated in the city center. Some regions are always in an advantageous position regardless of traffic conditions. The impact of some social‐economic factors on healthcare accessibility was analyzed, which exhibited significant spatial heterogeneity. Hospital deserts for different vulnerable populations were identified. Besides children with massive hospital deserts at the city fringe, other vulnerable populations have no distinct disadvantage. These results offer profound comprehension of healthcare inequality to assist in healthcare resources management and policy‐making in Beijing.

ACS Style

Shize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS 2021, 25, 1504 -1521.

AMA Style

Shize Gong, Yong Gao, Fan Zhang, Lan Mu, Chaogui Kang, Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS. 2021; 25 (3):1504-1521.

Chicago/Turabian Style

Shize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. 2021. "Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement." Transactions in GIS 25, no. 3: 1504-1521.

Data descriptor
Published: 11 January 2021 in Scientific Data
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Measuring the geographical distribution of economic activity plays a key role in scientific research and policymaking. However, previous studies and data on economic activity either have a coarse spatial resolution or cover a limited time span, and the high-resolution characteristics of socioeconomic dynamics are largely unknown. Here, we construct a dataset on the economic activity of mainland China, the gridded establishment dataset (GED), which measures the volume of establishments at a 0.01° latitude by 0.01° longitude scale. Specifically, our dataset captures the geographically based opening and closing of approximately 25.5 million firms that registered in mainland China over the period 2005–2015. The characteristics of fine granularity and long-term observability give the GED a high application value. The dataset not only allows us to quantify the spatiotemporal patterns of the establishments, urban vibrancy, and socioeconomic activity, but also helps us uncover the fundamental principles underlying the dynamics of industrial and economic development.

ACS Style

Lei Dong; Xiaohui Yuan; Meng Li; Carlo Ratti; Yu Liu. A gridded establishment dataset as a proxy for economic activity in China. Scientific Data 2021, 8, 1 -9.

AMA Style

Lei Dong, Xiaohui Yuan, Meng Li, Carlo Ratti, Yu Liu. A gridded establishment dataset as a proxy for economic activity in China. Scientific Data. 2021; 8 (1):1-9.

Chicago/Turabian Style

Lei Dong; Xiaohui Yuan; Meng Li; Carlo Ratti; Yu Liu. 2021. "A gridded establishment dataset as a proxy for economic activity in China." Scientific Data 8, no. 1: 1-9.

Research article
Published: 02 January 2021 in International Journal of Geographical Information Science
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As the essence of urban spatiotemporal interaction systems, hubs and centers empower cities to enhance socioeconomic prosperity and sustainability. However, a city manifests a time-evolving spatial interaction network with latent temporal interactions and irregular spatial partitions. This phenomenon is termed the spatiotemporal inconsistency problem. The aggregate, single-layer network model is defective for capturing the importance of locations in such time-evolving spatial interaction systems. This article therefore proposes a novel multilayer network model based on the nature of inherent spatial and temporal dependencies of urban interactions. First, the spatial agglomeration and the temporal correlation are explicitly modeled in multilayer networks for alleviating the spatiotemporal inconsistency problem. Secondly, generalized centrality metrics from a single-layered static network to the multi-layered dynamic network are acquired in order to discover grouped hub locations over time. Lastly, the capability of the proposed method is evaluated by an empirical analysis of the taxi mobility networks of Beijing, China, from 2012 to 2017. The empirical analysis indicates that the proposed method enables the identification of typical hub locations clustered in space and stable over time. This ability is essential to understand the centrality of locations informed by noisy and inconsistent data in their spatial and temporal dimensions.

ACS Style

Chaogui Kang; Zhuojun Jiang; Yu Liu. Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality. International Journal of Geographical Information Science 2021, 1 -22.

AMA Style

Chaogui Kang, Zhuojun Jiang, Yu Liu. Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality. International Journal of Geographical Information Science. 2021; ():1-22.

Chicago/Turabian Style

Chaogui Kang; Zhuojun Jiang; Yu Liu. 2021. "Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality." International Journal of Geographical Information Science , no. : 1-22.

Research article
Published: 30 December 2020 in Transactions in GIS
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An anomalous geographical region refers to a collection of spatially aggregated objects whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighbors. The detection of anomalous regions plays an important role in spatial data mining. However, the requirement of user‐specified parameters for spatial neighborhood construction and anomalous region discovery will inevitably result in the omission or misjudgment of spatial anomalies; it is still challenging to detect arbitrarily shaped anomalous regions in an objective way. Inspired by the data field theory, this study models spatial anomaly degree by considering the distance decay effect and develops an approach for the objective detection of significantly anomalous regions from spatial sampling points. First, constrained Delaunay triangulation is employed to construct reasonable and stable spatial neighborhoods by quantifying the spatial distribution characteristics of sampling points. On this basis, a Gaussian function is adopted for the measurement of spatial anomaly degree considering both distance decay effect and non‐spatial attribute value differences, based upon which anomalous objects can be captured. Finally, treating each anomalous object as a seed, a multidirectional optimization method is developed to identify arbitrarily shaped anomalous regions, and a Monte Carlo simulation is employed to further test the statistical significance of anomalous regions. Experiments on both simulated and real‐world datasets demonstrate that the proposed approach outperforms existing methods in terms of both accuracy and sufficiency for anomalous region detection.

ACS Style

Xuexi Yang; Min Deng; Yan Shi; Jianbo Tang; Zhou Huang; Yu Liu. Detecting statistically significant geographical anomalous regions from spatial sampling points by coupling Gaussian function and multidirectional optimization. Transactions in GIS 2020, 1 .

AMA Style

Xuexi Yang, Min Deng, Yan Shi, Jianbo Tang, Zhou Huang, Yu Liu. Detecting statistically significant geographical anomalous regions from spatial sampling points by coupling Gaussian function and multidirectional optimization. Transactions in GIS. 2020; ():1.

Chicago/Turabian Style

Xuexi Yang; Min Deng; Yan Shi; Jianbo Tang; Zhou Huang; Yu Liu. 2020. "Detecting statistically significant geographical anomalous regions from spatial sampling points by coupling Gaussian function and multidirectional optimization." Transactions in GIS , no. : 1.

Journal article
Published: 17 December 2020 in Landscape and Urban Planning
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Crime and perception of safety are two intertwined concepts affecting the quality of life and the economic development of a society. However, few studies have quantitatively examined the difference between the two due to the lack of granular data documenting public perceptions in a given geographic context. Here, by applying a pre-trained scene understanding algorithm, we infer the perception of safety score of streetscapes for census block groups in the city of Houston using a large number of Google Street View images. Then, using this inferred perception of safety, we create “perception bias” categories for each census block group. These categories capture the level of mismatch between people’s visually perceived safety and the actual crime rates. This measure provides scalable guidance in deciphering the relationship between the built environment and crime. Finally, we construct a series of models to examine the “perception bias” with static and dynamic urban factors, including socioeconomic features (e.g., unemployment rate and ethnic compositions), urban diversity (e.g., number and diversity of Points of Interest), and urban livelihood (i.e., hourly count of visitors). Analytical and numerical results suggest that the association between characteristics of urban space and “perception bias” over crime could be paradoxical. On the one hand, neighborhoods with a higher volume of day-time visitors appear more likely to be safer than it looks (low crime rate and low safety score). On the other hand, those with a higher volume of night-time visitors are likely to be more dangerous than it looks (high crime rate). The findings add further knowledge to the long-recognized relationship between built environment and crime as well as highlight the perception of safety in cities, which in turn enhances our capacity to design urban management strategies that prevent the emergence of extreme “perception bias”.

ACS Style

Fan Zhang; Zhuangyuan Fan; Yuhao Kang; Yujie Hu; Carlo Ratti. “Perception bias”: Deciphering a mismatch between urban crime and perception of safety. Landscape and Urban Planning 2020, 207, 104003 .

AMA Style

Fan Zhang, Zhuangyuan Fan, Yuhao Kang, Yujie Hu, Carlo Ratti. “Perception bias”: Deciphering a mismatch between urban crime and perception of safety. Landscape and Urban Planning. 2020; 207 ():104003.

Chicago/Turabian Style

Fan Zhang; Zhuangyuan Fan; Yuhao Kang; Yujie Hu; Carlo Ratti. 2020. "“Perception bias”: Deciphering a mismatch between urban crime and perception of safety." Landscape and Urban Planning 207, no. : 104003.

Journal article
Published: 03 December 2020 in Scientific Reports
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Understanding quantitative relationships between urban elements is crucial for a wide range of applications. The observation at the macroscopic level demonstrates that the aggregated urban quantities (e.g., gross domestic product) scale systematically with population sizes across cities, also known as urban scaling laws. However, at the mesoscopic level, we lack an understanding of whether the simple scaling relationship holds within cities, which is a fundamental question regarding the spatial origin of scaling in urban systems. Here, by analyzing four extensive datasets covering millions of mobile phone users and urban facilities, we investigate the scaling phenomena within cities. We find that the mesoscopic infrastructure volume and socioeconomic activity scale sub- and super-linearly with the active population, respectively. For a same scaling phenomenon, however, the exponents vary in cities of similar population sizes. To explain these empirical observations, we propose a conceptual framework by considering the heterogeneous distributions of population and facilities, and the spatial interactions between them. Analytical and numerical results suggest that, despite the large number of complexities that influence urban activities, the simple interaction rules can effectively explain the observed regularity and heterogeneity in scaling behaviors within cities.

ACS Style

Lei Dong; Zhou Huang; Jiang Zhang; Yu Liu. Understanding the mesoscopic scaling patterns within cities. Scientific Reports 2020, 10, 1 -11.

AMA Style

Lei Dong, Zhou Huang, Jiang Zhang, Yu Liu. Understanding the mesoscopic scaling patterns within cities. Scientific Reports. 2020; 10 (1):1-11.

Chicago/Turabian Style

Lei Dong; Zhou Huang; Jiang Zhang; Yu Liu. 2020. "Understanding the mesoscopic scaling patterns within cities." Scientific Reports 10, no. 1: 1-11.

Journal article
Published: 07 October 2020 in Remote Sensing
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Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

ACS Style

Zhou Huang; Houji Qi; Chaogui Kang; Yuelong Su; Yu Liu. An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data. Remote Sensing 2020, 12, 3254 .

AMA Style

Zhou Huang, Houji Qi, Chaogui Kang, Yuelong Su, Yu Liu. An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data. Remote Sensing. 2020; 12 (19):3254.

Chicago/Turabian Style

Zhou Huang; Houji Qi; Chaogui Kang; Yuelong Su; Yu Liu. 2020. "An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data." Remote Sensing 12, no. 19: 3254.

Editorial
Published: 25 September 2020 in Journal of Hydrology
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ACS Style

Erik Nixdorf; Min Chen; Hui Lin; Xiaohui Lei; Olaf Kolditz. Monitoring and modeling of water ecologic security in large river-lake systems. Journal of Hydrology 2020, 591, 125576 .

AMA Style

Erik Nixdorf, Min Chen, Hui Lin, Xiaohui Lei, Olaf Kolditz. Monitoring and modeling of water ecologic security in large river-lake systems. Journal of Hydrology. 2020; 591 ():125576.

Chicago/Turabian Style

Erik Nixdorf; Min Chen; Hui Lin; Xiaohui Lei; Olaf Kolditz. 2020. "Monitoring and modeling of water ecologic security in large river-lake systems." Journal of Hydrology 591, no. : 125576.

Journal article
Published: 14 September 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The spatial concentration of human activity is a crucial indication of socio-economic vitality. Accurately mapping activity volumes is fundamental to support regional sustainable development. Current approaches rely on mobile positioning data, which record information about human daily activity but are inaccessible in most cities due to privacy and data sharing concerns. Alternative methods are needed to provide more generalized predictions on extensive areas while maintaining low cost. This study demonstrates how remote sensing imagery can be used through an end-to-end deep learning framework for reliable estimates of human activity volumes. The neighbour effect, representing the inherent nature of spatial autocorrelation in the volumes, is incorporated to improve the network. The proposed model exhibits strong predictive power and demonstrates great explainability of physical environment on variations of activity volumes. Landscape interpretations based on hierarchical features provide both object-based and region-based insights into the co-evolvement of landscape and human activity. Our findings indicate the possibility of extensively predicting activity volumes, especially in areas with limited access to mobile data, and provide support for the promising framework to better comprehend broad aspects of human society from observable physical environments.

ACS Style

Xiaoyue Xing; Zhou Huang; Ximeng Cheng; Di Zhu; Chaogui Kang; Fan Zhang; Yu Liu. Mapping Human Activity Volumes Through Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5652 -5668.

AMA Style

Xiaoyue Xing, Zhou Huang, Ximeng Cheng, Di Zhu, Chaogui Kang, Fan Zhang, Yu Liu. Mapping Human Activity Volumes Through Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5652-5668.

Chicago/Turabian Style

Xiaoyue Xing; Zhou Huang; Ximeng Cheng; Di Zhu; Chaogui Kang; Fan Zhang; Yu Liu. 2020. "Mapping Human Activity Volumes Through Remote Sensing Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5652-5668.

Research article
Published: 26 August 2020 in International Journal of Geographical Information Science
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Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit.

ACS Style

Yi Bao; Zhou Huang; Linna Li; Yaoli Wang; Yu Liu. A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media. International Journal of Geographical Information Science 2020, 35, 639 -660.

AMA Style

Yi Bao, Zhou Huang, Linna Li, Yaoli Wang, Yu Liu. A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media. International Journal of Geographical Information Science. 2020; 35 (4):639-660.

Chicago/Turabian Style

Yi Bao; Zhou Huang; Linna Li; Yaoli Wang; Yu Liu. 2020. "A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media." International Journal of Geographical Information Science 35, no. 4: 639-660.

Research article
Published: 18 August 2020 in International Journal of Geographical Information Science
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Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments. Many applications categorize such data into groups and compare the characteristics across groups. The intergroup differences vary with spatio-temporal units, and the essential is to identify the spatio-temporal units with apparently different data characteristics. However, spatio-temporal dependence, data variety, and the complexity of tasks impede an effective unit assessment. Inspired by the applications to extract critical image components based on explainable artificial intelligence (XAI), we propose a spatio-temporal layer-wise relevance propagation method to assess spatio-temporal units as a general solution. The method organizes input data into an extensible three-dimensional tensor form. We provide two means of labeling the spatio-temporal tensor data for typical geographical applications, using temporally or spatially relevant information. Neural network training proceeds to extract the global and local characteristics of data for corresponding analytical tasks. Then the method propagates classification results backward into units as obtained task-specific importance. A case study with taxi trajectory data in Beijing validates the method. The results prove that the proposed method can evaluate the task-specific importance of spatio-temporal units with dependence. This study also attempts to discover task-related knowledge using XAI.

ACS Style

Ximeng Cheng; Jianying Wang; Haifeng Li; Yi Zhang; Lun Wu; Yu Liu. A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence. International Journal of Geographical Information Science 2020, 1 -24.

AMA Style

Ximeng Cheng, Jianying Wang, Haifeng Li, Yi Zhang, Lun Wu, Yu Liu. A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence. International Journal of Geographical Information Science. 2020; ():1-24.

Chicago/Turabian Style

Ximeng Cheng; Jianying Wang; Haifeng Li; Yi Zhang; Lun Wu; Yu Liu. 2020. "A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence." International Journal of Geographical Information Science , no. : 1-24.