I-index for quantifying an urban location's irreplaceability

A location's irreplaceability refers to the degree of difficulty with which it can be replaced by other locations. For example, the irreplaceability of airports, hospitals, and ATMs varies, and that between hospitals is also different. They differ in both the number of users and the extent of service area. Quantifying the location's irreplaceability provides guidance for urban planning, such as siting of public resources. Existing methods for quantifying an urban location's irreplaceability do not consider human activity at the location, therefore the revealed irreplaceability may deviate from the resident's perceptions. To address this issue, we use origin-destination flows to reflect human activity. We propose a flow-based locational measure, I-index, to quantify the location's irreplaceability. It can be viewed as ‘H-index of flow’ where we regard locations as scientists, flows as papers. I-index of a location is the maximum value of i such that at least i flows with a length of at least α 1 i meters have reached this location, where α is the conversion factor that can be determined adaptively from the flow dataset. I-index elegantly combines the flow volume and length into a single value. The effectiveness of the I-index is validated by simulation experiments. Two case studies show that the hospital's irreplaceability strongly correlates with the hospital bypass behavior and locations with strongly mixed urban functions are more irreplaceable. The implications for urban planning are further discussed.     

Spatial pattern of population mobility among cities in China: Case study of the National Day plus Mid-Autumn Festival based on Tencent migration data

Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. “Tencent Migration” big data can real-timely, dynamically, completely and systematically record population flow routs using LBS device. Through gathering residents daily mobility among 299 cities in China during the period of “National Day–Mid-Autumn Festival” (NDMAF) vacation (from September 30 to October 8) in 2017 in “Tencent Migration” and defining three periods with “travel period, journey period, return period”, this paper is designed to analyze and explore the characteristics and spatial patterns of daily flow mobility cities from the perspective of population daily mobility distribution levels, flow distribution layers network aggregation, spatial patterns and characteristics of the complex structure of the flow network. Results show that “Tencent migration” big data clearly discovers the temporal-spatial pattern of population mobility in China during the period of NDMAF. The net inflow of population showed a diamond shaped with cross frame support in each period, the four nodes of the diamond contain Beijing, Shanghai, Guangzhou and Xi'an. Main mobility assembling centers are distributed in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing, and those centers have strong coherence with those urban hierarchies. Most cities are in a state of “relative equilibrium” in the population flow, and clear hierarchical structure and level distinction can be identified. Spatial patterns of population mobility present obvious core-periphery structures. The Dali-Hegang line exhibits a significant network of spatial differences in terms of boundary divisions. In this context, the spatial distribution of urban network could be summarized as “dense in the East and sparse in the West”, and the core linkages of urban network could be characterized as “parallel in the East and series in the West”. The whole network exhibits a typical “small world” network characteristic, which shows that China's urban population flow network has high connectivity and accessibility during the period of NDMAF. The network has a distinct “community” structure in the local area, including 2 national communities, 2 regional communities and 3 local-level communities.     

基于决策树方法的云南省森林分类研究

森林分类对于理解森林生态系统结构和功能具有重要意义。由于云南省地形和森林类型复杂,首先按云南省的16个行政区划将全省Landsat TM影像分为对应的16个区域。以TM波段1~5和7,以及由植被指数、缨帽变换、主成分变换、DEM组成的18个变量组,统计训练样本光谱值均值变化和光谱值与频率间的关系。利用交点计算公式计算类间最佳分类界点进而建立决策树,逐一分离各区的所有森林类型,将分类结果合并得到云南省阔叶林、针叶林和针阔混交林类型分布图。最后将分类结果与监督分类中的最大似然比法的分类结果进行对比。结果表明:监督分类的总体分类精度为74.39%,Kappa系数为0.63,决策树方法的总体分类精度为86.61%,Kappa系数为0.80,说明决策树方法可以提取高精度的云南省森林类型,进而为该区域森林叶面积指数和生物量反演等研究提供基础数据支持。     

最小平方卷积反演算法及其在桥梁动挠度测量中的应用

本文给出的最小平方卷积反演方法,利用了输入信号的时限约束,用Hilbert空间中的正交投影的方法,得到最小平方意义下的最优解.本文利用该方法成功地从惯性传感器信号中反演出桥梁动挠度信号,并给出与其他方法的比较,证明了该方法的有效性,为桥梁动挠度测试提供了一种新的手段.     

Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light

Satellite\|derived nighttime light (NTL)data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity.This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables.The recently composited cloud\|free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS)provides spatially detailed observations of human settlements.We quantitatively estimated socioeconomic development inequalities across 30 provinces andmunicipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP)and population census data.We quantitatively investigated relations between NTL,GDP,and population using a linear regression model.Our results suggest that NTL have significant positive correlations with GDP and population at different levels.Several inequality coefficients were derived from VIIRS data and statistical data at multiple spatial scales.NTL\|derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels.Our study of provinces and municipalities revealed that county\|level inequality was more significant than city\|level.The results of population\|weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with eastern regions.Our findings suggest that given the timely and spatially explicit advantages of VIIRS,NTL data are capable of providing comprehensive information regarding inequality at multiple levels,which is not possible through the use of traditional statistical sources.     

Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Land surface temperature (LST) is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer (IASI) and atmospheric infrared sounder (AIRS) data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ (DRRI) has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm^?1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI- and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSTs are no more than 2 K, while the differences between AIRS- and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred (–12.89 K), the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed.     

An object-based analysis filtering algorithm for airborne laser scanning

Ground filtering is a key process to derive digital terrain models from airborne laser scanning data. Although many methods have been developed to tackle the filtering problem, it has not been fully solved so far. Current algorithms mainly focus on neighbourhood-based or directional filtering approaches. A new object-based analysis (OBA) method is proposed in this article. First, a grid index algorithm accelerates access to unorganized cloud points. Then, a segmentation algorithm is deployed based on the index, and objects are obtained. A filtering logic that utilizes the objects' characteristics is designed. Following this, the performance of the method is comprehensively tested using publicly available International Society for Photogrammetry and Remote Sensing (ISPRS) test data sets for nine urban and six rural regions, and the results are compared to those of eight other algorithms. The OBA method implemented in this article reveals good results without scene-wise optimization of the parameters, and it ranks third or fourth in most of the cases.     

A framework for modelling spatio-temporal informal settlement growth prediction

Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, future informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries.A novel generic framework is proposed in this paper for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within an area under investigation. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that influence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influential spatial features, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements.     

Single tree species classification from Terrestrial Laser Scanning data for forest inventory

Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the forestry domain for forest inventory, the development of software tools for the automatic measurement of forest inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.