scholarly journals Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation

2021 ◽  
Vol 13 (8) ◽  
pp. 1574
Author(s):  
Yuping Wang ◽  
Zehao Shen
Keyword(s):  
Spatial Structure ◽  
Density Estimation ◽  
Spatial Resolution ◽  
Kernel Density Estimation ◽  
Urban Areas ◽  
Infrared Imaging ◽  
Kernel Density ◽  
Urban System ◽  
Urban Spatial Structure ◽  
Nighttime Light

Nighttime light (NTL) data are increasingly used in urban studies and urban planning owing to their strong connection with human activities, although the detection capacity is limited by the spatial resolution of older data. In the present study, we comparedthe results of extractions of urban built-up areas using data obtained from the first professional NTL satellite Luojia 1-01 with a resolution of 130 m and the Visible Infrared Imaging Radiometer Suite (VIIRS). We applied an analyzing framework combing kernel density estimation (KDE) under different search radii and threshold-based extraction to detect the boundary and spatial structure of urban areas. The results showed that: (1) Benefiting from a higher spatial resolution, Luojia 1-01 data was more sensitive in detecting new emerging urban built-up areas, thus better reflected the spatial structure of urban system, and can achieve a higher extraction accuracy than that of VIIRS data; (2) Combining with a proper threshold, KDE improves the extraction accuracy of NTL data by making use of the spatial autocorrelation of nighttime light, thus better detects the scale of the spatial pattern of urban built-up areas; (3) A proper searching radius for KDE is critical for achieving the optimal result, which was 1000 m for Luojia 1-01 and 1600 m for VIIRS in this study. Our findings indicate the usefulness of the KDE method in applying the upcoming high-resolution NTL data such as Luojia 1-01 data in urban spatial analysis and planning.

scholarly journals Dengue Vector Density Incident and Its Implication to Urban Livability

2020 ◽  
Author(s):  
Nuriah Abd Majid ◽  
Muhammad Rizal Razman ◽  
Sharifah Zarina Syed Zakaria ◽  
Nurafiqah Muhamad Nazi
Keyword(s):  
Quality Of Life ◽  
Urban Area ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Urban Areas ◽  
Management Strategies ◽  
Kernel Density ◽  
Kuala Lumpur ◽  
Future Management

Abstract Background: Malaysia's population is set to reach 33.10 million by the end of 2020. About 75% of the population of Malaysia lived in urban areas and cities. The metropolitan area of Greater Kuala Lumpur had a population of more than seven million that year, making it the largest urban area in Malaysia. Kuala Lumpur as the city centre for Greater Kuala Lumpur has been ranked as Southeast Asia's second most liveable city after Singapore. The livable city imperative is relevant because Malaysia's urbanization process is moving towards harmonization with the principles of sustainable development. Livable city involves many interdependent factors contributing to the urban quality of life. With their complete physical and social infrastructures, the urban types are an essential basis for improving the quality of life of the urbanites. However, increasing population and rapid land-use changes led to the emergence of vector-borne diseases such as dengue in an urban area. Prolong dengue outbreaks will reduce livability in urban areas. Therefore, this study aims to look at the density of dengue distribution in Bandar Baru Bangi town in 2014, 2015, 2016 and 2017.Methods: The study uses data provided from the Ministry of Health Malaysia and shows the focus of dengue cases in residential and industrial areas of Bandar Baru Bangi town. Spatial analysis using Geographical Information System (GIS) was applied to identify the locality of dengue incidence within the study area. Spatial statistical analysis of dengue cases used Kernel Density Estimation to distinguish dengue hotspots from the distribution of the exact location of dengue cases reported in Bandar Baru Bangi town.Results: Kernel density estimation showed the dengue hotspots concentrated on the east of Bandar Baru Bangi town. The results found that the highest density was in 2015 was 605 to 706 points per square kilometres. This study also discovers that most of the hotspots constructed were located in the residential area of Bandar Baru Bangi.Conclusions: This study is essential to help local authorities eradicate dengue in urban areas for future management strategies; therefore, this study is vital to help local authorities eradicate dengue in urban areas for future management strategies.

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

Sign in / Sign up

Export Citation Format

Share Document