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Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.
Guie Li; Zhongliang Cai; Yun Qian; Fei Chen. Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality. Land 2021, 10, 648 .
AMA StyleGuie Li, Zhongliang Cai, Yun Qian, Fei Chen. Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality. Land. 2021; 10 (6):648.
Chicago/Turabian StyleGuie Li; Zhongliang Cai; Yun Qian; Fei Chen. 2021. "Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality." Land 10, no. 6: 648.
Bozhao Li; Zhongliang Cai; Mengjun Kang; Shiliang Su; Shanshan Zhang; Lili Jiang; Yong Ge. A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks. International Journal of Geographical Information Science 2020, 35, 717 -740.
AMA StyleBozhao Li, Zhongliang Cai, Mengjun Kang, Shiliang Su, Shanshan Zhang, Lili Jiang, Yong Ge. A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks. International Journal of Geographical Information Science. 2020; 35 (4):717-740.
Chicago/Turabian StyleBozhao Li; Zhongliang Cai; Mengjun Kang; Shiliang Su; Shanshan Zhang; Lili Jiang; Yong Ge. 2020. "A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks." International Journal of Geographical Information Science 35, no. 4: 717-740.
Poverty remains one of the long-term chronic dilemmas facing the sustainable development of human society during the 21st century. The spatiotemporal dynamics of poor regions, particularly in developing countries, is crucial for realizing fundamental sustainable development goals (SDGs). For decades, many scholars have sought to accurately measure, identify and alleviate poverty at different geographical scales. However, reliable data about the estimation of poverty remain scarce for developing countries, hindering efforts to accurately identify poverty. This paper utilizes the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) sensor nighttime light imagery to identify poor counties in China from 1992 to 2013. Using 16 statistical and spatial features extracted from this nighttime light imagery and using 96 poor counties and 96 nonpoor counties from 2010 as the classification sample, we describe the spatiotemporal dynamics of poor counties based on a random forests approach. Our study finds that the number of poor counties is decreasing in a fluctuating pattern and that contiguous poverty-stricken areas are becoming fragmented. The reduction in poor counties exhibits a manner of moving horizontally from the eastern regions to the central and western parts of China, while the number of poor counties in the central and western regions has decreased around the central cities or areas. The Aihui-Tengchong Line is not the dividing line in the distribution of poor counties in China, which means that China's poor can also be found in areas with relatively high population density. Together, the findings reveal that the key to reducing regional poverty is the development of regional economies and the implementation of national macro policies. This paper provides references for formulating antipoverty strategies for each county and offers new insights into poverty estimation and regional sustainable development for other developing countries.
Guie Li; Liyun Chang; Xiaojian Liu; Shiliang Su; Zhongliang Cai; Xinran Huang; Bozhao Li. Monitoring the spatiotemporal dynamics of poor counties in China: Implications for global sustainable development goals. Journal of Cleaner Production 2019, 227, 392 -404.
AMA StyleGuie Li, Liyun Chang, Xiaojian Liu, Shiliang Su, Zhongliang Cai, Xinran Huang, Bozhao Li. Monitoring the spatiotemporal dynamics of poor counties in China: Implications for global sustainable development goals. Journal of Cleaner Production. 2019; 227 ():392-404.
Chicago/Turabian StyleGuie Li; Liyun Chang; Xiaojian Liu; Shiliang Su; Zhongliang Cai; Xinran Huang; Bozhao Li. 2019. "Monitoring the spatiotemporal dynamics of poor counties in China: Implications for global sustainable development goals." Journal of Cleaner Production 227, no. : 392-404.
Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.
Shiyan Pang; Xiangyun Hu; Mi Zhang; Zhongliang Cai; Fengzhu Liu. Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images. Remote Sensing 2019, 11, 729 .
AMA StyleShiyan Pang, Xiangyun Hu, Mi Zhang, Zhongliang Cai, Fengzhu Liu. Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images. Remote Sensing. 2019; 11 (6):729.
Chicago/Turabian StyleShiyan Pang; Xiangyun Hu; Mi Zhang; Zhongliang Cai; Fengzhu Liu. 2019. "Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images." Remote Sensing 11, no. 6: 729.
The goal of the present study is to demonstrate that high-poverty counties and robust classification features can be identified by machine learning approaches using only DMSP/OLS night-time light imagery. To accomplish this goal, a total of 96 high-poverty and 96 non-poverty counties were classified using 15 statistical and spatial features extracted from night-time light imagery in China in 2010 formed a training set for identifying high-poverty counties. Seven machine learning approaches were adopted to classify high-poverty counties, and five feature importance measures were used to select robust features. The resulting metrics, including the user’s (>63%), producer’s (>66%) and overall (>82%) accuracies of the poor county identification (probability of poverty greater than 0.6), show that the seven machine learning approaches used in this paper exhibit good performance, although some differences exist among the approaches. The order of feature importance reveals that the relative importance of each feature differs among the models; however, the important features remain consistent. The nine most important features ranked in each approach are relatively robust for poverty identification at the county level. Both spatial feature and statistical features calculated in part from the central tendency, degree of dispersion, and the distribution of the night-time light data were identified as indispensable robust features in all the approaches, indicating that the complex social phenomenon of poverty requires analysis from different aspects. Previous studies that utilized primarily night-time light imagery applied single features related to the central tendency or the distribution features of the imagery; this study provides a new method and can act as a reference for feature selection and identification of high-poverty counties using night-time light imagery and has potential applications across several scientific domains.
Guie Li; Zhongliang Cai; Xiaojian Liu; Ji Liu; Shiliang Su. A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery. International Journal of Remote Sensing 2019, 40, 5716 -5736.
AMA StyleGuie Li, Zhongliang Cai, Xiaojian Liu, Ji Liu, Shiliang Su. A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery. International Journal of Remote Sensing. 2019; 40 (15):5716-5736.
Chicago/Turabian StyleGuie Li; Zhongliang Cai; Xiaojian Liu; Ji Liu; Shiliang Su. 2019. "A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery." International Journal of Remote Sensing 40, no. 15: 5716-5736.
Poverty remains one of the most serious chronic dilemmas facing civilization and economic development in the 21st century. How to accurately measure, identify and alleviate poverty have been urgent topics on different geographical scales for decades. Based on census data at the county level from 2000 to 2010 in China, principal component analysis was used to establish an integrated multidimensional poverty index (IMPI) for geographical identification of poverty-stricken counties using an indicators system guided by a sustainable livelihoods framework. Further cluster analysis, spatial analysis and a self-organizing map show obvious spatiotemporal heterogeneity of multidimensional poverty across the 2311 counties in China. The results demonstrate that the counties with higher IMPI are concentrated and conjointly distributed in southwest China, north of central China and southeast of northwest China in mountainous regions and plateaus. Longitudinal comparisons demonstrate that the degree of multidimensional poverty has relatively decreased across China from 2000 to 2010, but regional disparities continue to expand and new aspects are emerging. In addition, compared with 2000, the number of counties with multidimensional poverty in 2010 increased in northeast China and decreased in central China. Many counties have experienced generally increased levels in certain domains of poverty. The relative contribution of each indicator to the IMPI also provides important references for formulating and implementing poverty policy. Quantile regression was utilized to explore the application of the IMPI in assessing environmental inequality. The result indicates that many poverty-stricken and developed counties are exposed to poor air quality. The accurate identification of geographical and spatiotemporal patterns of poverty in China can lead to the implementation of anti-poverty strategies. This paper also offers new insights into poverty measurement for other developing countries.
Guie Li; Zhongliang Cai; Ji Liu; Xiaojian Liu; Shiliang Su; Xinran Huang; Bozhao Li. Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications. Social Indicators Research 2019, 144, 1099 -1134.
AMA StyleGuie Li, Zhongliang Cai, Ji Liu, Xiaojian Liu, Shiliang Su, Xinran Huang, Bozhao Li. Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications. Social Indicators Research. 2019; 144 (3):1099-1134.
Chicago/Turabian StyleGuie Li; Zhongliang Cai; Ji Liu; Xiaojian Liu; Shiliang Su; Xinran Huang; Bozhao Li. 2019. "Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications." Social Indicators Research 144, no. 3: 1099-1134.
Taxi is a core component of urban transit systems. Since they can provide more time-saving and convenient service than many other transit options, taxis have a certain passenger base. The analysis of taxi ridership can be used to better understand the travel mobility of passengers and the traffic structure of urban areas. In previous studies, taxi trajectory data have been widely used, especially in exploring taxi ridership, and point-of-interest (POI) data are usually used to evaluate the land-use type of a certain sub-district. On the basis of preceding research, this paper uses taxi trajectory data within the long time scale of one week. Five traffic factors are taken into consideration: pick-ups, drop-offs, and the ratio of pick-ups to drop-offs, pick-up probability and drop-off probability. The research model is divided into weekdays and weekends. For the calculation of probabilities, an index termed the Area Crossing Index is proposed to reflect the taxi cardinality and accessibility of a region. At the same time, POI and demographic data are used as explanatory variables. In this study, we also take the business hours of POIs into consideration. In order to explore the ridership in each hour, hierarchical clustering is used to determine the similarity characteristics of hourly dependent variables. Then, stepwise linear regression is used to screen and evaluate coefficients without collinearity. Finally, geographically weighted regression is adopted to evaluate spatial variability, and the coefficients of common explanatory variables on weekdays and weekends are examined. At the end of this paper, the causes of common explanatory factors on weekdays and weekends for each traffic factor are discussed. This paper also analyzes ridership by combining all the results of dependent variables and proposes some suggestions for taxi scheduling.
Bozhao Li; Zhongliang Cai; Lili Jiang; Shiliang Su; Xinran Huang. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression. Cities 2019, 87, 68 -86.
AMA StyleBozhao Li, Zhongliang Cai, Lili Jiang, Shiliang Su, Xinran Huang. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression. Cities. 2019; 87 ():68-86.
Chicago/Turabian StyleBozhao Li; Zhongliang Cai; Lili Jiang; Shiliang Su; Xinran Huang. 2019. "Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression." Cities 87, no. : 68-86.
In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as “newly built”, “taller”, “demolished”, and “lower” by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.
Shiyan Pang; Xiangyun Hu; Zhongliang Cai; Jinqi Gong; Mi Zhang. Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images. Sensors 2018, 18, 966 .
AMA StyleShiyan Pang, Xiangyun Hu, Zhongliang Cai, Jinqi Gong, Mi Zhang. Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images. Sensors. 2018; 18 (4):966.
Chicago/Turabian StyleShiyan Pang; Xiangyun Hu; Zhongliang Cai; Jinqi Gong; Mi Zhang. 2018. "Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images." Sensors 18, no. 4: 966.