基于卫星遥感和POI数据的人口空间化研究——以广州市为例

以广州市为例,基于NPP/VIIRS夜间灯光、土地利用、POI（Points of Interest）等自然地理和社会经济因素,构建了人口空间化指标体系,采用主成分赋权法确定人口分布权重,利用GIS技术对人口统计数据进行了空间化处理。结果显示：综合考虑了自然地理和社会经济因素的人口空间化结果与真实的人口空间格局相吻合,空间分辨率为30 m,且相对误差绝对值＜25%的乡镇有62个,所占比例为36.47%;而不加POI数据得到的人口空间化结果,相对绝对值＜25%的乡镇有33个,所占比例约为20%,精度明显降低。结果表明：1）综合考虑NPP/VIIRS夜间灯光、土地利用、POI等自然地理和社会经济因素,有助于实现精度较高的人口空间化结果;2）将能够反映微观细节信息的POI数据引入人口空间化研究,扩展了人口空间化的数据源,并且可以提高人口空间化结果的精度。     

收缩城市住宅价格空间分异及影响机制——以鹤岗市为例

收缩城市的经济社会问题逐渐凸显,住宅市场发展呈现出消极态势,房价变化成为社会关注的焦点。在此背景下,论文以典型收缩城市——黑龙江省鹤岗市为例,基于住宅小区、夜间灯光、POI数据,利用Kriging插值和多尺度地理加权回归模型等方法,结合鹤岗市空间增长与收缩情况分析住宅价格空间分异特征及影响机制。结果显示：① 鹤岗市住宅价格呈圈层模式分布,核心—外围分异程度强。高水平住宅价格小区集聚在核心区内且分布规模小,中高值圈层扩散态势弱;外围工矿型辖区收缩情况相对严重,住宅价格水平低且波动小。② 城市发展状态与住宅价格水平存在空间相关性。不同收缩程度区域经济发展态势、居民收入水平、人口减少与老龄化、保障房建设规模等宏观因素差异影响着住宅价格总体空间分异格局。③ 城市内部区域的增长或收缩状态影响微观设施的作用效果。发展中心的区位优势对增长型区域住宅价格的正向作用更强,高水平公共服务对核心区和城市北部收缩区域住宅价格的增长有明显积极效应,企业工厂的分布对增长型区域住宅价格的负向影响更突出,各因素相互联系、交互影响,共同作用于住宅价格空间分异。     

2007—2019年中国城市土地价格的空间分化

土地价格是城市综合发展水平的反映,厘清中国城市土地价格的时空演变特征及其成因,有助于从宏观上把握地区间的发展差异。本文以地级行政区为研究对象,系统分析2007—2019年中国332个城市综合土地价格和住宅、商服、工矿仓储用地价格的演变特征及驱动因素,主要结论如下：① 2007—2019年间中国城市综合地价呈现显著的上涨趋势,由2007年的392.34元/m²上升至2019年的1357.31元/m²,年均增长80.73元/m²;地价水平在空间上呈现“东南高,西北低”的格局。② 三种主要地类的价格及增速高低依次为：住宅用地>商服用地>工矿仓储用地,发展逐渐分化,差距不断扩大。③ 不同等级城市的土地价格逐渐分化为三类：高速增长型（一线城市）、平稳增长型（新一线城市和二线城市）和缓慢增长型（三线城市、四线城市和五线城市）,呈现出明显的“马太效应”。④ 人均地区生产总值、人口密度和普通中小学学校数越高的城市,各类土地价格越高;本地人均地区生产总值、第二产业占比和第三产业占比越高的城市,其相邻城市的住宅和商服地价越高,工矿仓储地价越低。     

夜间灯光数据和兴趣点数据结合的建成区提取方法

提出一种夜间灯光数据和兴趣点(POI)数据相结合的建成区提取方法,根据NPP/VIIRS影像的亮度、纹理信息和POI数据的密度信息,分别利用阈值法对两种数据进行建成区提取,并应用数学形态学方法对提取结果进行融合。该方法利用POI数据位置准确、与建成区分布高度相关等特点,有效弥补了夜间灯光数据分辨率较低与灯光溢出问题,获取到较为准确的建成区边界。选取深圳、广州和惠州3个不同形态的城市,将该文方法与其他方法的提取效果进行对比,证明该方法提取精度较高,适用于市级尺度的精细化城市建成区提取和城市扩张研究。     

基于遥感数据和POI数据的GDP空间化研究 ——以北京市为例

为了比较分析不同数据对格网化社会经济活动空间分布精度差异性的影响,以北京市为例,基于土地利用数据对第一产业空间建模,基于NPP/VIIRS夜间灯光数据和POI数据运用熵值赋权和代用数据空间展布的方法对第二、三产业空间建模,将第一产业和第二、三产业进行格网叠加,并与辖区GDP统计数据进行误差分析。结果表明:借助土地利用数据、NPP/VIIRS夜间灯光数据和POI数据的GDP空间模拟结果与真实分布格局较为一致,而使用土地利用数据和夜间灯光数据进行GDP空间化模拟的精度明显降低。因此,将反映细节信息的POI数据加入到GDP空间化研究后,能够进一步扩展GDP空间化的数据源,提高GDP空间化模拟精度。基于土地利用数据、NPP/VIIRS夜光数据和POI数据可以实现较高精度的GDP空间化模拟。     

基于主成分和机器学习的土壤有机质含量空间预测建模

协同环境变量与机器学习回归模型构建土壤有机质空间预测组合模型对养分精准管理具有重要意义,而多维变量间的信息冗余和相关性会导致模型训练时间过长、预测精度降低等问题。以陕西省咸阳市农耕区为例,选取高程、坡向、坡度、剖面曲率、平面曲率、地形起伏度、地形湿度指数、年均降水量、年均气温、归一化植被指数共10个环境变量,在主成分分析(Principal compo-nent analysis,PCA)、核主成分分析(Kernel principal component analysis,KPCA)方法特征提取基础上,组合随机森林(Random forest,RF)、支持向量回归机(Support vector regression,SVR)、K最近邻(K-nearest neighbor,KNN)机器学习模型进行土壤有机质含量空间预测。以单一模型作为对照,通过计算模型决定系数(Coefficient of determination,R~2)、均方根误差(Root mean square error,RMSE)和相对绝对误差(Relative absolute error,RAE),对不同模型的预测结果进行精度评价。结果表明:利用主成分提取方法和机器学习算法构建组合模型能消除变量间相关性,一定程度上提高土壤有机质含量预测模型精度。KPCA-RF模型对SOM含量预测精度高于其他模型,R~2、RMSE、RAE分别为0.791、1.970 g·kg~(-1)、50.100%,该模型良好的预测能力可以为土壤有机质含量的空间预测与制图提供科学依据。     

ARIMA-LSTM组合模型在基于SPI干旱预测中的应用——以青海省为例

开展干旱预测是有效应对干旱风险的前提基础。利用1958—2017年青海省38个气象站点逐日降水量数据计算多尺度标准化降水指数（SPI）,并建立了SPI序列自回归移动平均模型（ARIMA）、长短时记忆神经网络模型（LSTM）和基于二者优点提出的ARIMA-LSTM组合模型;对模型参数进行率定和验证后,利用所建立的模型,以西宁站点为例,对多尺度SPI值进行预测,借助均方根误差（RMSE）、平均绝对百分比误差（MAPE）和决定系数R²对所有预测模型的有效性进行判定。结果表明：ARIMA-LSTM组合模型在SPI1和SPI12的RMSE值分别为0.159 7和0.181 0,均低于ARIMA模型的1.265 4和0.293 3,说明ARIMA模型与ARIMA-LSTM组合模型对SPI的预测精度都与时间尺度有关,ARIMA模型的预测精度随着时间尺度的增加而逐渐提高;结合GIS并利用实测数据与模型的预测数据相比较说明ARIMA-LSTM组合模型相比于单一ARIMA模型的预测精度更高,且能够很好拟合不同时间尺度的SPI值。     

MODIS和Landsat时空融合影像在土壤盐渍化监测中的适用性研究——以渭干河—库车河三角洲绿洲为例

土壤盐渍化的遥感监测依赖于高时空分辨率影像,但受经费预算、卫星回访周期及天气的影响,高时空分辨率的遥感影像较难获取,这就限制了根据采样时间来获取对应时期遥感影像进行土壤盐渍化监测反演的应用。为此,提出融合MODIS和Landsat影像生成高时空分辨率影像来提取土壤盐渍化信息,为时空影像进行土壤盐渍化监测研究提供数据参考。以渭干河—库车河绿洲为研究区,利用增强型时空自适应融合率反射模型（Enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM）和灵活的时空融合模型（Flexible spatiotemporal data fusion,FSDAF）,对MODIS和Landsat影像进行时空融合,并基于融合影像数据构建了关于土壤电导率（EC）的随机森林（RF）预测模型,对比分析时空融合影像应用于土壤盐渍化遥感监测的适用性。结果表明：ESTARFM融合影像的特征波段反射率与Landsat验证影像对应波段反射率一致性决定系数R²（Red）=0.8066、R²（SWIR2）=0.8444;FSDAF融合影像的特征波段与Landsat验证影像对应波段反射率一致性决定系数R²（Red）=0.6999、R²（SWIR2）=0.7493;基于ESTARFM融合影像构建的EC值预测模型精度最高,R²=0.9268,基于FSDAF融合影像构建的EC值预测模型精度良好,R²=0.8987,基于验证影像构建的EC值预测模型R²=0.9103; ESTARFM模型的融合精度高于FSDAF模型,并且基于融合影像构建的EC值预测模型效果良好。     

基于随机森林的元胞自动机城市扩展模拟——以佛山市为例

本文提出一种基于随机森林的元胞自动机城市扩展(RF-CA)模型。通过在多个决策树的生成过程中分别对训练样本集和分裂节点的候选空间变量引入随机因素,提取城市扩展元胞自动机的转换规则。该模型便于并行构建,能在运算量没有显著增加的前提下提高预测的精度,对城市扩展中存在的随机因素有较强的容忍度。RF-CA模型可进行袋外误差估计,以快速获取模型参数;也可度量空间变量重要性,解释各空间变量在城市扩展中的作用。将该模型应用于佛山市1988-2012年的城市扩展模拟中,结果表明,与常用的逻辑回归模型相比,RF-CA模型进行模拟和预测分别能够提高1.7%和2.6%的精度,非常适用于复杂非线性特征的城市系统演变模型与扩展研究;通过对影响佛山市城市扩展的空间变量进行重要性度量,发现对佛山城市扩张模拟研究而言,距国道的距离与距城市中心的距离具有最重要的作用。     

基于地价监测信息的地价预测模型研究

实现对地价的全面监测、分析和预测,有助于城市基准地价的快速更新和政府对土地资源的宏观控制。过去的地价预警预报模型研究主要存在两点不足：一是没有建立科学完善的数学模型,也没有用多种数学模型相互验证预测结论;二是没有充分利用现有的信息,如地价监测体系的信息,致使预测地价水平与城市实际地价水平、预测走势与实际变化有偏差。该文利用地价监测体系的信息,建立马尔可夫链地价预测模型,并与时间序列模型、空间分布预测模型的预测结果相比较。马尔可夫链地价预测模型是概率分布式预测模型,适用于对地价变化进行科学的概率预测,预测结果与实际情况吻合较好。     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.

     

呼和浩特市旗县城镇基准地价 及影响因素比较分析

本文在介绍基准地价内涵的基础上,采用比较分析法从自然条件、社会经济条件等方面对比分析呼和浩特市5个旗县城镇基准地价的特点,阐述呼和浩特市城镇基准地价及其空间分异特征和城镇基准地价与其影响因素的关系。研究结论是城镇商业、住宅和工业用地的基准地价呈递减的趋势,商业、住宅、工业用地的平均基准地价比为4.22∶1.50∶1。商业用地的地价分布由于受到道路的影响,临路形成较高的基准地价区域,住宅用地基准地价由中心向外,级别和价格呈较均匀递减的变化规律,城镇规划因素在基准地价评估中具有重要作用。基准地价与区位的影响因素密切相关,各因素相互作用共同影响地价的高低。各旗县应积极改善地价影响因素,缩小基准地价的差距,促进区域经济的发展。该项研究为今后呼和浩特市土地市场的发展,引导土地合理、集约、高效利用,充分发挥土地的潜在价值起到促进作用。     

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.     

基于克里格回归分析和机器学习算法的日本福岛县土地价格估算与制图的比较分析

Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners.This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts.Since 2005,the Ministry of Land,Infrastructure,and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations.Although this data is useful,it does not provide complete information at every site for all market participants.Therefore,estimating and mapping land prices based on sound statistical theories is required.This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms.Land use,elevation,and socioeconomic factors,including population density and distance to railway stations,were used for modeling.Results show the superiority of the random forest algorithm.Overall,land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.     

Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model

Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithm by considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes.     

Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.     

A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area

Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas.     

长江中游城市群空间结构的多分形特征

在城市群越来越演化为多尺度、多区域复杂系统背景下,有必要引入多分形理论与方法研究其空间结构。本文基于2018年NPP-VIIRS夜间灯光数据,计算长江中游城市群整体及其局部的多分维谱,根据谱线分析不同尺度下长江中游城市空间结构的多分形特征。结果显示：① 长江中游城市群夜间灯光容量维在整体和局部都出现双标度现象。② q < -5.5时,整体广义关联维谱线突破理论上限2,在q > 0时,武汉城市圈和环长株潭城市群的分维显著较高。③ 整体的局部分维谱和武汉城市圈、环长株潭城市群、环鄱阳湖城市群、宜荆荆城市群局部分维谱表现为单峰偏右。根据上述结果,得到和验证了以下结论：① 长江中游城市群区域一体化程度较低。② 长江中游城市群不同层级和区域的空间结构差异显著,呈现出多尺度复杂特征。③ 长江中游城市群在不同尺度中均倾向于中心集聚式发展。研究揭示多分形模型能够从尺度依赖视角有效揭示巨型城市群空间结构的复杂性及其背后的问题,具有很好的理论探索和实践分析前景。     

南京市主城区住宅地价的时空演变

城市地价在空间、时间分布上具有较强的关联性和特殊性,随着城市建设的快速发展和土地市场的不断发育,城市地价的时空变化日趋复杂。本文以南京市主城区为例,基于城市地价动态监测数据,运用统计分析方法和克里金插值方法,研究城市住宅地价时空演变特征。研究表明:从宏观上讲,地价时间上演变受宏观经济影响较大,大的经济形势、房地产市场的变动,对地价变化影响显著。从中观上讲,地价空间上演变主要体现在城市内部的区位条件、交通条件、城市规划、公用设施状况和环境条件等的影响。上述两者共同作用于地价的变化,并且两者相互关联、密不可分。通过城市地价的时空演变研究,以期快速、直观、准确地反映城市地价变化,为城市地价的宏观调控、合理利用土地资源提供参考。     

基于多源数据的武汉市多中心空间结构识别

研究城市不同要素空间集聚水平与层级差异、明确城市各职能中心分布,对城市多中心空间结构的发展引导和规划调控具有重要意义。论文分别采用兴趣点(points of interest, POI)、夜间灯光和路网3种数据,利用局部等值线树算法,识别武汉市多中心空间结构。研究表明：① 武汉不同城市要素的总体集聚水平不均衡,其中在汉阳集聚水平较低,在汉口和武昌集聚水平较高,特别是二环以内区域;② 武汉中央活动区发展比较均衡,内部各城市要素高度集聚,随着向外扩展,部分城市要素易于在局部集聚形成城市中心;③ 结合武汉圈层发展布局,城市中心沿环线的“商-住-工”职能分布模式逐步确立,其中内环以及二环区域已形成稳定的商服中心;二环附近综合组团内部出现了较具代表性的居住中心;三环外的沌口和武钢主导发展工业,是典型的工业中心。