Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

An approach for measuring spatial similarity among COVID-19 epicenters

Most countries and territories worldwide are affected by coronavirus disease 2019(COVID-19),and some cities have become known as epicenters owing to high outbreaks.Because of the changeable and unknown nature of the virus,managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods.It would be even more beneficial if they analyzed the experiences of similar cities.The similarity of such cities could be examined within a geographic information system based on various criteria.This study investigated the similarities among eight cities-Wuhan,Tehran,Bergamo,Madrid,Paris,Daegu,New York,and Berlin-in terms of the COVID-19 situation(target)in these locations based on proximity factors,weather,and demographic criteria.First,the factor and target layers were prepared,and then similar cities were identified using a similarity model and different distance metrics.The results were aggregated using the Copeland method because of the different outcomes for each metric.The most similar city was identified for each selected city,and its similarity level was determined based on these criteria.The results suggested the following pairs of similar cities:Wuhan-Berlin,Tehran-Berlin,Daegu-Wuhan,Bergamo-Madrid,Paris-Madrid,and New York-Paris based on COVID-19 related data up to 15 April 2020(target T1),and Daegu-Wuhan,Tehran-Madrid,Bergamo-Paris,Berlin-Paris,and New York-Madrid up to 8 December 2021(target T2)with a minimum and maximum similarity rate of 82.85%and 92.36%,respectively.For similar cities,the most similar factors among the proximity criteria are the distance from bus and metro stations;among weather,the criteria are humidity and pressure;and among demographics,the criteria are male and female population ratios,literacy ratio,and death ratio from asthma and cancer,with a minimum and maximum difference of 0%and 64.94%,respectively.In addition,according to the random forests ranking results(with root mean squared error=0.23),temperature,distance from the bank,and gender were the most important criteria for the eight studied cities.Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city.

Suitable Region for Flue-cured Tobacco (Nicotiana glauca L.) Planting Based on Spatial Scene Similarity

[Objective] The aim was to establish a model based on spatial scene similarity, for which soil, slope, transport, water conservancy, light, social economic factors in suitable planting areas were all considered. A new suitable planting area of flue-cured tobacco was determined by comparison and analysis, with consideration of excellent area. [Method] Totaling thirty natural factors were chosen, which were clas- sified into nine categories, from Longpeng Town （LP） and Shaochong Town （SC） in Shiping County in Honghe Hani and Yi Autonomous Prefecture. [Result] According to weights, the factors from high to low were as follows： soil〉light〉elevation〉slope〉 water conservancy〉transport〉baking facility〉planting plans over the years〉others. The similarity of geographical conditions in the area was 0.894 3, which indicated that the planting conditions in the two regions are similar. If farmer population in unit area, farmland quantity for individual farmer, labors in every household, activity in planting flue-cured tobacco and work of local instructor were considered, the weights of different factors were as follows： farmer population in unit area〉farmland quantity for individual farmer〉farmers＇ activity in planting flue-cured tobacco〉educational back- ground〉labor force in every household〉instructor〉population of farmers＇ children at- tending school. The similarity of geographical conditions was 0.703 1, which indicated that it is none-natural factors that influence yield and quality of flue-cured tobacco. [Conclusion] According to analysis on suitable planting area of flue-cured tobacco based on assessment of spatial scene similarity, similarity of growing conditions in two spatial scenes can be analyzed and evaluated, which would promote further exploration on, influencing factors and effects on tobacco production.

Measurement of Similarity for Spatial Directions Between Areal Objects

Similarity for spatial directions plays an important role in GIS. In this paper, the conventional approaches are analyzed. Based on raster data areal objects, the authors propose two new methods for measuring similarity among spatial directions. One is to measure the similarity among spatial directions based on the features of raster data and the changes of distances between spatial objects, the other is to measure the similarity among spatial directions according to the variation of each raster cell centroid angle. The two methods overcome the complexity of measuring similarity among spatial directions with direction matrix model and solve the limitation of small changes in direction. The two methods are simple and have broader applicability.

Shape Similarity Measures of Linear Entities

The essential of feature matching technology lies in how to measure the similarity of spatial entities.Among all the possible similarity measures,the shape similarity measure is one of the most important measures because it is easy to collect the necessary parameters and it is also well matched with the human intuition.In this paper a new shape similarity measure of linear entities based on the differences of direction change along each line is presented and its effectiveness is illustrated.