## 3 Results  
### Data Measurement and Amendment  
POI data have the problem of overlapping classification and repeated counting in terms of the number of public toilets, resulting in large errors in POI data records in some areas. In this paper, the public toilets inside large buildings such as shopping malls, hotels and motels, fast food restaurants, railway stations, and hospitals are classified as dependent public toilets. Other public toilets in single buildings on both sides of green parks, schools, scenic spots, and roads are classified as independent public toilets. For the quantitative correction of POI public toilet data, this paper will be conducted separately from dependent public toilets and independent public toilets. Of which thirty dependent public toilets inside large buildings and ten independent public toilets inside parks are selected for investigation (Figure 2). Due to the small amount of POI data information in 2008, this paper only modified the POI public toilet data in 2018. Considering the spatial autocorrelation of the data, the [PERSON]'s I index was used for validation. The [PERSON]'s I index is an important indicator of spatial autocorrelation [46]. The [PERSON]'s I values of POI data for public toilets in 2008 and 2018 were 0.013 and \(-\)0.008. There was no significant spatial autocorrelation between the data. There is a significant error in the number of public toilets counted. A counting unit of public toilets should contain two essential components: men's toilets and women's toilets. Some of them include special toilets used by the disabled, maternal, and infants, and all three constitute a quantity unit of public toilets. The point information of dependent public toilets in the POI database is men's, women's, maternal, and infant, and the accessible toilets separately. Therefore, the quantity information of subsidiary public toilets in the POI data needs to be modified to ensure the accuracy of the balance analysis. Among one thousand six hundred and fifty-five POI data, there are seven hundred and ninety-four dependent public toilets, covering six hundred and seventy-five public toilets in shopping malls and railway stations and one hundred and nineteen public toilets in hotels and fast-food restaurants. Among them, the number of public toilets in shopping malls and shopping centers is the largest, and the density is the greatest. Therefore, the POI data have a large error. This paper takes six hundred and seventy-five POI-dependent public toilets of shopping malls and railway stations as the basis for address information, excluding samples that could not be investigated in the field due to closure and renovation. The thirty large building samples cover twenty-eight shopping malls and two railway stations. Among them, nine shopping malls and two railway stations did not cover public toilets with special needs, such as maternal and infant rooms and disabled toilets. A comprehensive field survey was conducted on thirty data samples to obtain information on the number of public toilets, the availability of public toilets for special needs, sanitation and cleaning facilities, and the number of people accommodated. The number of POI data in the thirty samples was used as the independent variable. The number of public toilets from the field survey data was used as the dependent variable for data fitting. The actual number of public toilets for the nine samples of special needs public toilets was slightly larger than one-third of the number of POI public toilets. After the constructed exponential, logarithmic, linear, and polynomial fits, the linear fit gave the best results with a goodness of fit R\({}^{2}\) of 0.87 and a range of residuals of [\(-\)2.79, 2.83]. The actual number of public toilets in the twenty-one samples without special needs is much larger than one-half of the number of POI public toilets. The constructed fitting expression obtained the best polynomial fit result with a goodness of fit R\({}^{2}\) of 0.94 and a range of residuals of [\(-\)2.84, 2.75] (Figure 3).