
Qiliang LiuCentral South University | CSU · Department of Geo-informatics
Qiliang Liu
Doctor of Philosophy in GIScience
About
58
Publications
15,497
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
894
Citations
Introduction
Skills and Expertise
Education
September 2011 - September 2014
September 2008 - June 2011
September 2004 - June 2008
Publications
Publications (58)
The stationarity assumption of geostatistical methods is difficult to satisfy in practice. To overcome this limitation, this study proposed a geometric and statistical coupling strategy for modeling spatial dependence structures and developed a generalized Yang Chizhong filtering and interpolation (GYangCZ) method without the assumption of stationa...
The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom consider the physical mechanisms of water quality variation, which degrades the accuracy and stability of the prediction results. Hence, we develop a ph...
In multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes which is difficult to satisfy in practice. We developed...
Space-time interpolation is a fundamental task of space-time data analysis. Modeling of space-time dependencies in geospatial data plays a key role in space-time interpolation. When geospatial data is non-stationary and sparsely distributed, modeling of space-time dependencies is still challenging. On that account, this study developed a space-time...
Identifying anomalies from geochemical data by modeling of the background and statistical evaluation of anomalies is a major concern in geochemical exploration. This study developed a novel method (namely YangScan) for extracting geochemical anomalies by using Yang Chizhong filtering and a spatial scan statistic. The Yang Chizhong filtering, a prog...
Geospatial knowledge graphs provide critical technology for integrating geographic information and semantic knowledge, which are very useful for geographic data analysis. As the scale of geospatial knowledge graphs continues to grow, the distributed management of geospatial knowledge graphs is becoming an inevitable requirement. Geospatial knowledg...
Detecting hotspots in origin–destination (OD) flows is crucial for understanding spatial interaction patterns of geographical phenomena. Because of the influence of global spatial autocorrelation, local flow hotspots are often misidentified or overlooked. To address this issue, this study proposes a weighted moving average method based on binomial...
Determining the optimal number of regions is a challenging issue in regionalization. Although cluster validity indices developed for non-spatial clustering have been used to determine the optimal number of regions, spatial contiguity constraints for regionalization are often neglected. Consequently, different regionalization results can share the s...
The presence of global spatial autocorrelation usually leads to the spurious identification of spatial hotspots and hinders the identification of local hotspots. Despite the use of statistical methods to address global spatial autocorrelation in spatial hotspot detection, accurately modeling global spatial autocorrelation structure without the stat...
For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement pa...
Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing multi-source geospatial big data, e...
Identifying spatial communities in vehicle movements is vital for sensing human mobility patterns and urban structures. Spatial community detection has been proven to be an NP-Hard problem. Heuristic algorithms were widely used for detecting spatial communities. However, the spatial communities identified by existing heuristic algorithms are usuall...
Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we...
A bivariate flow cluster is a group of two types of spatial flows, where both types of flows have high (or low) values, or one type of flow has a high value while the other has a low value. Identifying bivariate flow clusters aids in understanding the complex interactions between different flow patterns. Detecting bivariate flow clusters remains ch...
Detecting regional co-location patterns on urban road networks is challenging because it is computationally prohibitive to search all potential co-location patterns and their localities, and effective statistical methods for evaluating the prevalence of regional co-location patterns are lacking. To overcome these challenges, this study developed an...
In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It rema...
Identifying clusters from individual origin–destination (OD) flows is vital for investigating spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non-uniform flow clusters from network-constrained OD flows continues to be a challenge. This study proposes a shared nearest-neighbor-based clustering method (SNN_flow) for i...
Identifying spatial communities in massive vehicle trajectory data greatly facilitates the understanding of spatial interactions in a city. However, it is still challenging to identify irregularly shaped and statistically significant spatial communities in vehicle movements. To overcome this challenge, we develop a spatial scan statistic based on a...
Multilevel co-location patterns embedded in spatial datasets are difficult to discern due to the complexity of neighboring relationships among spatial features. The neighboring relationships are used to determine whether instances of different spatial features are located in close geographic proximity. When spatial features are distributed unevenly...
Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate...
The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natu...
Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statis...
The clustering of spatio-temporal events has become one of the most important research branches of spatio-temporal data mining. However, the discovery of clusters of spatio-temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio-temporal windo...
Regional spatial co-location patterns refer to subsets of spatial features that often co-occur in close geographical proximity in certain localities of space. Discovering regional spatial co-location patterns is still very challenging because it is difficult to specify appropriate thresholds for prevalence measures without prior knowledge and to de...
Space–time series prediction plays a key role in the domain of geographic data mining and knowledge discovery. In general, the existing methods of space–time series prediction can be divided into two main categories: statistical machine learning methods. Comparatively, machine leaning methods have obvious advantages with respect to handling nonline...
Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study devel...
Time series clustering algorithms have been widely used to mine the clustering distribution characteristics of real phenomena. However, these algorithms have several limitations. First, they depend heavily on prior knowledge. Second, the algorithms do not simultaneously consider the similarity of spatial locations, spatial-temporal attribute values...
Spatio-temporal clustering is an important technique for mining dynamic patterns of geographical phenomena, which aims to discover groups of data so that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. Spatio-temporal clustering has been a hot topic in the field of spatio-temporal data mining and knowledge d...
Recognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approach...
Spatial heterogeneity has been regarded as an important issue in space–time prediction. Although some statistical methods of space–time predictions have been proposed to address spatial heterogeneity, the linear assumption makes it difficult for these methods to predict geographical processes accurately because geographical processes always involve...
Space–time series can be partitioned into space–time smooth and space–time rough, which represent different scale characteristics. However, most existing methods for space–time series prediction directly address space–time series as a whole and do not consider the interaction between space–time smooth and space–time rough in the process of predicti...
Spatial co-location pattern mining aims to discover a collection of Boolean spatial features, which are frequently located in close geographic proximity to each other. Existing methods for identifying spatial co-location patterns usually require users to specify two thresholds, i.e. the prevalence threshold for measuring the prevalence of candidate...
Spatial co-location patterns discovery aims to detect spatial features whose instances are frequently located in geographic proximity. Such patterns can reveal unknown regularity in geographic phenomena and they are helpful for decision-making. However, due to the little prior knowledge, it is difficult to specify thresholds for neighbor distance a...
In massive Twitter datasets, tweets deriving from different domains, e.g., civil unrest, can be extracted to constitute spatio-temporal Twitter events for spatio-temporal distribution pattern detection. Existing algorithms generally employ scan statistics to detect spatio-temporal hotspots from Twitter events and do not consider the spatio-temporal...
Spatial outlier detection is a research hot spot in the field of spatial data mining. Because of the lack of specific research on spatial point events, this study presents an adaptive approach for spatial point events outlier detection (SPEOD) using multilevel constrained Delaunay triangulation. First, the spatial proximity relationships between sp...
Map is a kind of powerful means to help people in understanding the objective world. The key function of map is to transmit spatial information. The measurement of spatial information of maps dates back to 1960s, when the information theory of communication was introduced to the field of cartography. The introduction led to a new branch of cartogra...
Spatial anomalies may be single points or small regions whose non-spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi-constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models s...
Mining spatial co-location patterns plays a key role in spatial data mining. Spatial co-location patterns refer to subsets of features whose objects are frequently located in close geographic proximity. Due to spatial heterogeneity, spatial co-location patterns are usually not the same across geographic space. However, existing methods are mainly d...
Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly developed for space-time processes that exhibit stationarity in space and time. It is still challenging t...
Spatial hierarchical clustering methods considering both spatial proximity and attribute similarity play an important role in exploratory spatial data analysis. Although existing methods are able to detect multi-scale homogeneous spatial contiguous clusters, the significance of these clusters cannot be evaluated in an objective way. In this study,...
Considering the attempts to model spatiotemporal topological relationships between moving object trajectories, the conceptual and computational framework for moving objects along a road network has not received much attention. This paper aims to draw an improved model based on Region Connection Calculus (RCC) theory to represent the spatiotemporal...
Abstract:Clustering analysis of uncertain data plays a key role in spatial data mining.A series of uncertain data clustering algorithms
have been proposed based on the traditional partitioning and density-based clustering algorithms.Although the application
of uncertain data clustering in geoscience has attracted more and more attentions,an objecti...
A fundamental element of exploratory spatial data analysis is the discovery of clusters in a spatial point dataset. When clusters with distinctly different local densities exist, the determination of suitable density level is still an unsolved problem. On that account, an iterative detection and removal method is proposed in this study. In each ste...
An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstacles and facilitators into account without addition...
Geometrical properties and attributes are two important characteristics of a spatial object. In previous spatial clustering studies, these two characteristics were often neglected. This paper addresses the problem of how to accommodate geometrical properties and attributes in spatial clustering. A new density-based spatial clustering algorithm (DBS...
The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of differe...
Spatio-temporal clustering has been a hot topic in the field of spatio-temporal data mining and knowledge discovery. It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world. However, existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneitie...
In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation (ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the edges of Delaunay triangulation and a novel spatial proximity definition based upon Delaunay triangulation to detect spatial clusters. Normally, this algorithm can automa...
Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential spatial association rules and outliers among the spatial data. Most existing spatial clustering algorithms only utilize the spatial distance or local density to find the spatial clusters in a spatial database, without...
Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential rules and outliers among the spatial data. Most existing spatial clustering methods cannot deal with the uneven density of the data and usually require predefined parameters which are hard to justify. In order to overc...
Most spatial clustering methods utilize fixed thresholds in the process of clustering which assume homogeneous (or even) distribution of the spatial points rather than inhomogeneous (or uneven) scattering. However, in many practical applications, spatial points usually distribute unevenly (in different density), which makes the fixed threshold meth...