On Statistical Measures for Data Quality Evaluation

Most GIS databases contain data errors. The quality of the data sources such as traditional paper maps or more recent remote sensing data determines spatial data quality. In the past several decades, different statistical measures have been developed to evaluate data quality for different types of data, such as nominal categorical data, ordinal categorical data and numerical data. Although these methods were originally proposed for medical research or psychological research, they have been widely used to evaluate spatial data quality. In this paper, we first review statistical methods for evaluating data quality, discuss under what conditions we should use them and how to interpret the results, followed by a brief discussion of statistical software and packages that can be used to compute these data quality measures.

Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters:The case of Shenyang city

Respiratory infection is the main route for the transmission of coronavirus pneumonia,and the results have shown that the urban spatial environment significantly influences the risk of infection.Based on the Wells-Riley model of respiratory infection probability,the study determined the human respiratory-related parameters and the effective influence range;extracted urban morphological parameters,assessed the ventilation effects of different spatial environments,and,combined with population flow monitoring data,constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells.In the empirical study in Shenyang city,a severe cold region,urban morphological parameters,population size,background wind speed,and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios.The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant.The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables.At the same time,the change in human body spacing beyond 1 m had a minor influence on the risk of infection.Among the urban morphological parameters,building height had the highest correlation with the risk of infection,while building density had the lowest correlation.The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results.The overlap rate between medium or higher risk areas and actual cases was 78.55%.The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements.The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities.