Exploring space-time models for West Nile virus mosquito abundance data

This paper explores variant space-time models for log-transformed West Nile virus (WNv) mosquito data, which explicitly account for both local environmental conditions and complex dependent structures. Four space-time models take various forms to accommodate correlated structure in space and time, nested data, and nonstationarity. The average WNv mosquito abundance is captured by a global trend across all four models, but different model assumptions are imposed on the stochastic component of the proposed models: a simple multivariate linear regression model with independent and identical errors, a site-specific linear mixed model with temporally correlated errors, a week-specific linear mixed model with spatially correlated errors, and a local space-time kriging model. In a case study, the predictive performance of the four models was assessed using data collected in 2007 and 2008 for the Greater Toronto Area by the mosquito surveillance program of Ontario Ministry of Health and Long-term Care: the local space-time kriging model outperforms others, but closely followed by a site-specific linear mixed model with temporal correlation. Our findings suggest that the predictive accuracy of space-time WNv mosquito abundance models can be enhanced by explicitly taking into account spatiotemporal correlation, nonstationarity, and the data collection procedure, such as surveillance design, based on sound understanding of mosquito behavior and population dynamics.     

Local Spatiotemporal Modeling of House Prices: A Mixed Model Approach

The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales.     

Toward space-time buffering for spatiotemporal proximity analysis of movement data

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.     

Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.     

Spatio-temporal regression kriging for modelling urban NO2 concentrations

ABSTRACT

Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO₂) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO₂ concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m^?3, a Mean Error of ?0.03 μg m^?3 and a Mean Absolute Error of 7.29 μg m^?3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure.     

中国省际人口迁移短期预测分析

人口迁移是一个时空路径依赖过程,同时受迁移存量和周边迁移状况影响。当前人口迁移预测大多建立在时间序列模型之上,重点考虑迁移流在时间维度上的联系,忽视了其中的时空关联。该文将特征向量时空滤波方法与普通泊松模型相结合,考虑迁移流中可能存在的时空滞后和同期两种结构,对1985-2015年不同时段的中国省际人口迁移流数据进行建模和估计,并利用拟合程度较优的模型预测2015-2025年省际人口迁移的发展趋势。结果表明:1)特征向量时空滞后和同期滤波泊松模型均能较好地模拟研究时段省际人口迁移过程,自1985年以来我国省际人口迁移流不仅受迁出地和迁入地经济、社会等因素影响,也与过去迁移存量及周边迁移流密切相关;2)区域人口规模和GDP对迁移流的“推—拉”作用符合预期,地区人口规模较高和经济发展水平较低会促进人口外迁,反之则有利于吸引外来人口;3)与特征向量时空滞后滤波泊松模型相比,时空同期模型更便于捕捉省际人口迁移过程中的时空路径依赖特性,意味着当前人口迁移流的发展更易受到同时期周边迁移流的影响,表现出明显的羊群效应;4)预计2015-2025年我国省际迁移总量持续增加,呈现更集聚的空间模式,高迁入与高迁出区域在空间上相连,形成一条南北贯通的“高密度迁移地带”。将特征向量时空滤波模型拓展到人口迁移这一空间相互作用领域,可为当前构建更加完善的要素市场化配置体制机制等提供科学参考。     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比

以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。     

Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices

By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.     

基于XGBoost的多源降水数据融合方法研究

气象站点观测降水难以精确反映降水时空分布与变化,而雷达降水存在复杂地形区域精度不高等问题。为了最大限度发挥两者的优势,文章以广东省北部山区为研究区域,选择2018-08-26—30一次暴雨过程为研究对象,结合地形、与海岸线距离、植被指数、经纬度等地表辅助参量,分析地面站点降水与地表辅助参量、雷达降水的相关关系,利用XGBoost算法与克里金插值方法,构建地面-雷达日降水数据融合模型,得到了空间分辨率为1 km的日降水融合数据集。此外,采用多元线性回归（LM）与克里金插值方法,实现了地面-雷达日降水数据的融合,并利用地面降水数据分别对XGBoost与LM日降水融合性能进行精度验证。结果表明：1）地面降水与雷达降水存在显著的正相关,地面降水与地表辅助参量之间的相关性随时间变化;2）XGBoost预测精度整体上高于LM预测结果;经模型残差校正后,XGBoost融合模型的精度整体上优于LM融合模型,这是因为XGBoost方法在捕捉地面降水与地表辅助参量、雷达降水之间关系性能上优于LM方法。     

Using geographically weighted regression kriging for crop yield mapping in West Africa

Geographical information systems support the application of statistical techniques to map spatially referenced crop data. To do this in the optimal way, errors and uncertainties have to be minimized that are often associated with operations on the data. This paper applies a spatial statistical approach to upscale crop yields from the field level toward the scale of Burkina Faso. Observed yields were related to the Normalized Difference Vegetation Index derived from SPOT-VEGETATION. The objective was to quantify the uncertainties at the subsequent steps. First, we applied a point pattern analysis to examine uncertainties due to the sampling network of field surveys in the country. Second, geographically weighted regression kriging (GWRK) was applied to upscale the yield observations and to quantify the corresponding uncertainty. The proposed method was demonstrated with the mapping of sorghum yields in Burkina Faso and results were compared with those from regression kriging (RK) and kriging with external drift using a local kriging neighborhood (KEDLN). The proposed method was validated with independent yield observations obtained from field surveys. We observed that the lower uncertainty range value increased by 39%, and the upper uncertainty range value decreased by 51%, when comparing GWRK with RK and KEDLN. Moreover, GWRK reduced the prediction error variance as compared to RK (20 vs. 31) and to KEDLN (20 vs. 39). We found that climate and topography had a major impact on the country’s sorghum yields. Further, the financial ability of farmers influenced the crop management and, thus, the sorghum crop yields. We concluded that GWRK effectively utilized information present in the covariate datasets and improved the accuracies of both the regional-scale mapping of sorghum yields and was able to quantify the associated uncertainty.     

Urban Expansion and Spatiotemporal Relationships with Driving Factors Revealed by Geographically Weighted Logistic Regression

Urbanization improves our lives but also threatens human health and sustainable development. Revealing the spatiotemporal pattern of urban expansion and spatiotemporal relationships with driving forces, especially in terms of the ubiquitous and fast growing small city, is a crucial prerequisite to solving these problems and realizing sustainable development. Kunshan, China was used as a case study here. Eleven variables from four aspects covering physical, socioeconomic, accessibility and neighborhood were selected, and logistic regression and geographically weighted logistic regression modeling were employed to explore spatiotemporal relationships from 1991-2014. Results reveal that urban expansion in Kunshan shows an accelerating tendency with annual expansion from 2000-2014 four times higher than for 1991-2000. More importantly, the annual expansion rate of Kunshan of 28.42% in 2000-2014 is higher than that of a large city. Urban expansion and related factors have spatiotemporal varying relationships. From a global perspective, the closer to a city, town, main road and the higher the GDP, the more likely a region will undergo urbanization. Interestingly, the effect of population on urban expansion is decreasing, especially in developed areas, and the effect of distance to lake is enhanced. From a local perspective, the magnitude and even the sign of the coefficients vary across the study area. However, the range of the coefficient of GWLR is around that of the corresponding variable in LR, and the sign of most variables in GWLR is consistent with that of corresponding variables in LR. GWLR surpasses LR with the same explanatory variables in revealing regional differences and improving model reliability. Based on these findings, more attention should be given to small cities in China. Promoting the connotation of city culture and public services to realize New-type Urbanization and regional diversity policy in order to manage urban expansion scientifically are also recommended.     

基于MOD16的山西省地表蒸散发时空变化特征分析

基于MOD16全球蒸散发产品和气象站点实测数据,运用变异系数法、Sen趋势法等研究了山西省2000—2014年地表蒸散发ET、潜在蒸散发PET的空间分布特征、变化趋势及影响因素。结果表明：① MOD16蒸散产品与气象站点实测蒸散发之间具有良好的时空相关性(R ²=0.90),其产品精度可以满足山西省蒸散发时空分布研究的要求;②山西省多年平均ET、PET分别为816.77、1608.46 mm,年内变化表现为先增高后下降的“单峰”型分布,二者差值在5月、6月最大,此时山西省最为干旱;③ 全省年平均ET呈现西北低、东南高的分布特征,PET呈西南高、东北低的分布特征,二者差值整体上较大,表现为全省地表水分比较缺乏,其中忻州、吕梁西部最为严重;④ 全省近15 a来ET和PET的年际变化都比较小,整体上全省PET在增加,ET在相对减少,意味着近15 a来干旱情况在加剧;⑤ ET、PET的时空变化与诸多气象因子相关,在空间尺度上与降水、相对湿度密切相关,在时间尺度上与气温、降水关系最为密切。     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究

基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression, GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression, PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression, M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression, MLR)为基准比较3种GWR模型的精度。研究表明：(1)安徽省土壤pH具有空间异质性,且集聚特征明显。(2) 3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(R_adj²)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的R_adj^2<...     

A response to ‘A comment on geographically weighted regression with parameter-specific distance metrics’

In this article, we respond to ‘A comment on geographically weighted regression with parameter-specific distance metrics’ by Oshan et al. (2019), published in this journal, where several concerns on the parameter-specific distance metric geographically weighted regression (PSDM GWR) technique are raised. In doing so, we review the developmental timeline of the multiscale geographically weighed regression modelling framework with related and equivalent models, including flexible bandwidth GWR, conditional GWR and PSDM GWR. In our response, we have tried to answer all the concerns raised in terms of applicability, veracity, interpretability and computational efficiency of the PSDM GWR model.     

“双碳”目标下中国服务业碳强度时空交互特征及跃迁机制

为实现国家自主贡献承诺,如期达到“碳达峰、碳中和”目标,中国服务业的低碳发展是必然趋势。基于多种空间分析方法,从时空交互视角研究了中国服务业碳强度差异格局、空间关联、动态演化及跃迁机制。结果表明：(1) 2005—2019年中国服务业碳强度的总体差异存在动态收敛趋势,在空间上也呈现显著的聚类现象,且空间集聚水平逐渐趋于稳定。(2)在服务业碳强度局部空间结构与依赖方向上,西北与东北地区波动性较强,东部沿海地区相对稳定;在碳强度时空跃迁的过程中整体表现出一定的转移惰性,具有较强的空间依赖或路径锁定特征,其中中部、西部的多数地区始终保持高碳强度属性,是制约中国服务业协同减排的关键区域。(3)服务业碳强度的时空网络格局主要以正向关联为主,表现出较强的空间整合性,但少数邻接省域仍存在一定程度的时空竞争。(4)各地区服务业碳强度时空跃迁的驱动模式存在差异,其中,东部沿海省份主要受人口—城镇化制约模式的影响,西北、西南和东北的多数地区主要受技术—规制驱动模式的影响。自东南至西北,中国服务业碳强度的跃迁模式逐渐呈现出“同向制约—反向发展—同向发展”的阶梯递变格局。因此,政府减排政策的制定不仅应统筹考虑...     

北京市出租车运量分布的时空格局及生成机制

出租车是城市交通的重要组成部分,对其精细化的管控需要理解运量分布的时空特征和生成机制。通过北京市出租车大数据,采用系统聚类法将其15分钟时间片段归并为空间分布相似的时段,刻画出运量分布的时空格局,进而采用地理加权回归模型分析了生成机制。本研究揭示的出租车运量分布变化的时间点并不完全对应传统的整时点;各时段运量均呈现空间集聚的特征,但集聚的位置、面积明显不同,工作日不同时段运量集聚程度的差异较周末更大;不同时段出租车运量的影响因子有所差异,其中商业设施、房价、地铁和公交站密度、道路密度等因子通常较为显著。研究结果对时空上更精细的出租车运量预测、出租车分区分时段的政策管制和规划管理具有启示意义。