• Korean journal of applied entomology
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• v.61 no.4
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• pp.549-554
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• 2022

Life table-related studies in insect ecology have been an interesting topic for insect researchers. Two calculation methods are commonly applied to estimate the intrinsic rate of natural increase (r_m) in the life table statistics. The first method is to estimate an approximate r_m by dividing the natural logarithm of the net reproductive rate (R₀) by mean generation time (T) (namely mean generation time-based method). Another approach is to apply the Lotka-Euler equation derived from the population growth equation of Lotka-Volterra to estimate accurate r_m using the maximum likelihood method (Lotka-Euler equation-based method). In the latter case, there is a difference in the estimated r_m value when the initial age class of the target cohort was set to "0" or "1", which confused the application. In this short review, a brief history of the calculation process of the life table was reviewed. It was again confirmed in the Lotka-Euler equation-based method that the form of $\sum\limits_{x=1}^{w}e^{-rx}l_xm_x=1$ should be applied to estimate r_m when the first age class was set to zero, while the form of $\sum\limits_{x=0}^{w}e^{-r(x+1)}l_xm_x=1$ when set to one.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.