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Suicide is a serious negative social phenomenon. In tliis study, we used Pytlion technology to obtain suicide deatli data from a network and applied mathematical statistical and geographic spatial analyses to study the spatial-temporal characteristics of suicide deaths and the relationship between suicide rate and economic development in China from 2000 to 2018. Following conclusions were drawn from the results. (1) The number of suicide deaths in China is on tlie rise. Within a year, the high-incidence period of suicide deaths is from May to June, whereas the low-incidence period is from February to March. Witliin a month, the lsl, 10th, and 20th days have the highest incidences of suicide deaths. Witliin a day, 77.2% of the suicide deaths occur from 06:00 to 19: 00, and 09:00 and 15:00 were tlie peak times in which suicide deaths take place. (2) A total of 90.98% of tlie suicide deatlis occur in southeast China. The suicide rate is liiglier in the southeast than in the northwest, higher in the soutli tlian in the nortli, and decreases gradually from east to west. At county level, a relatively high suicide rate is seen in regions spanning from Great Kliingan Mountains to Yunnan Guizhou Plateau, from Qinling-Dabashan Mountains to Dabie Mountains, and from the coast of nortliem Jiangsu to Hainan Island. (3) Most areas in China present a low-grade suicide rate. However, low-grade areas appeared to change to high-grade areas during tlie period 2000 - 2018. The hotspots of suicide deatlis spread from east to west, except for the Beijing - Tianjin - Tangshan area, Yangtze River Delta, and Pearl River Delta, which have always been suicide hotspots. (4) The spatial and temporal characteristics of suicide deatlis in China are closely related to economic development, and on a city scale, tlie suicide rate has a significant positive correlation witli the per capita GDP and urbanization rate. Tlie impact of economic factors on suicide rate is greater on the soutlieast coast than on the northwest inland. An important conclusion from this study is that the gap between the rich and poor is a key factor, leading to psychological imbalance and suicidal behavior in the poor; tlierefbre, only tlie new development path based on common prosperity is the road for people to reach happiness and health. In addition, in this study, we prove that network suicide data, obtained using the web-crawler teclmology (Pytlion), not only have the same consistency and credibility as sampling statistics but also have a better spatiotemporal resolution, witli a temporal resolution of one hour and spatial resolution of a county. Therefore, by analyzing tliis spatiotemporal dataset, we can scientifically extract the time differences in suicide deaths at quarterly, monthly, daily, and hourly scales and the spatial differences in suicide deatlis at regional, provincial, and county scales. In the future, network suicide data may become an important data source for suicide research, and the use of the Internet to monitor suicidal behavior may become an important method of suicide intervention. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic spread globally at the start of 2020, resulting in a wide range of diffusion of infectious diseases. At present, SARS-CoV-2 is still prevalent worldwide, and it is of great practical significance to strengthen the study on the COVID-19 epidemic. Based on the COVID-19 confirmed cases and incidence, this article explored both the temporal and spatial distribution pattern of COVID-19 in Hunan Province and its influencing variables between January 21 and February 28, 2020 by using ArcGIS 10.3 and GeoDetector. The results show that: 1) The epidemic evolved with temporal phasing. The epidemic in Hunan Province can be divided into four basic stages: the diffusion period, peak period, decay period, and the stagnant period. The first COVID-19 confirmed case of 84.47% affected counties was imported from Wuhan;2) The epidemic spread as a result of transportation convenience. The confirmed COVID-19 cases were concentrated near major railway lines, which were also high-incidence areas. The epidemic occurred "early in prefecture-level cities and frequently in urban areas"; 3) the extent of the epidemic has distance attenuation. The COVID-19 cases and high-incidence areas were mainly distributed in the middle, north, and northeast of Hunan, in regions in proximity to Wuhan; 4) the epidemic was distributed with spatial aggregation. The agglomeration level of cases and incidences increased first and then remained high. The hot spots identified are in Changzhutan and Yueyang City, while the cold spots are in Xiangxi Prefecture; 5) the epidemic prevalence was of variable interaction. Wind-free, cold and dry weather was more conducive to the spread of the epidemic; population density, road network density and GDP were the main factors affecting the epidemic spatial differentiation; and the strongest variable interaction happened between natural and social factors. 6) the influencing variables of the COVID-19 epidemic have spatial heterogeneity. The impacts of spatial distance and population density have spatial heterogeneity on both the reported cases the total infections rates of COVID-19, spatial distance has the greatest impact in northeast Hunan, and population density has the greatest impact in northwest and northern Hunan; but the mobility of population and the number of health technicians only have spatial heterogeneity of impacts on the reported cases of COVID- 19, the impact of population mobility is diminishing from north to south in Hunan, and the influence of the number of health technicians decreases gradually with the Changsha-Zhuzhou-Xiangtan city agglomeration as the core to the outer circle. The paper's conclusions are consistent with other scholars' studies in other regions, confirming that the epidemic not only spreads significantly in neighboring areas, but there are also "jump spreads" to distant cities that rely on the transportation network in Hunan. It is evident that restricting the entry of residents of affected areas plays a key role in COVID-19 prevention and control, and moreover, allocating plentiful health technicians, executing rigorous transport quarantine, and adopting compatible preventing measures with local and temporal conditions, are the top priorities in effectively responding to the impact of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]