Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

Figures & data

Figure 1. Case study area of the research.

Table 1. Causative factors of skin cancer and their spatial units.

Skin Cancer Causative Factors		Spatial Unit of Needed Data	Spatial Unit of Available Data	Used Attribute Data
Environmental and Climate Parameters	Solar Energy	Polygon	Point	11 year average of solar energy at measurement stations
	Hours of Sunshine and UV radiation	Polygon	Point	11 year average of hours of sunshine and UV radiation at measurement stations
	Air Humidity	Point	Average arsenic content in drinking water	Polygon
	Air Pressure	Point	Annual average air humidity at measurement stations	Polygon
	Air Temperature	Point	Annual average air pressure at measurement stations	Polygon
Parameters related to the environment of life and work	Contact with Tar	Polygon	Polygon	Number of workers that are in contact with tar
	Contact with Oil	Polygon	Polygon	Number of workers that are in contact with oil
	Contact with Coal	Polygon	Polygon	Number of workers that are in contact with coal
	Contact with Sun	Polygon	Polygon	Number of workers that are directly exposed to the sun in their occupations
Nutrition habits	Fruit and Vegetable Usage	Polygon	Polygon	Percentage of income that each family uses to buy fruit and vegetables
	Red Meat Usage			Percentage of income that each family uses to buy red meat
	Poultry Usage			Percentage of income that each family uses to buy poultry
	Sea food Usage			Percentage of income that each family uses to buy seafood
	Tobacco and Cigarette Usage			Percentage of income that each family uses to buy tobacco and cigarettes
Usage of chemical fertilisers for agriculture		Polygon	Polygon	Ratio of nitrogen, phosphoric, and potash as fertiliser in cultivated area

Table 2. The considered time interval for causative factors in the model.

Factors	Time interval
Environmental and climatic parameters	11 years
Parameters related to environment of life and work	10 years
Nutrition habits	10 years
Usage of chemical fertilisers for agriculture	10 years

Figure 2. The maps of 11-yrars average of temperature, pressure, brightness sun shine hours, and humidity parameters.

Figure 3. Long-term records of surface UV data produced by ‘Longterm Multi-Sensoral UV Record’ project.

Figure 4. The modelling process flow diagram.

Figure 5. Chart of the distribution function of skin cancer data.

Table 3. Comparison between deviance residual of parameter selection methods.

Method of parameter selection	Acceptance level of deviance	Test result
Forward selection	141.3549	Accepted
Backward elimination	139.6979	Accepted
Stepwise selection	138.7203	Accepted

Figure 6. Chart showing the deviance residuals of the 3 regression methods (6.1. stepwise method, 6.2. backward elimination method, and 6.3. forward selection method).

Figure 7. Map of the residuals in the stepwise approach.

Table 4. Variables that emerged in the model along with their positive/negative sign in the model.

Variable description	The name of Parameter in Equation 18	Sign of coefficient
Number of farmers per township population	P1	Positive
Usage of potassium fertiliser in cultivated area	P6	Positive
Number of factory workers in contact with lead per township population	P14	Positive
Solar energy per area unit	P15	Positive
Average smoking expenses per family	P17	Positive
Average fruit/vegetable expenses per family	P18	Negative
Annual average temperature	P23	Positive
Annual bright sunshine hours	P24	Positive
Annual average atmosphere pressure	P25	Negative
Interactive effect of annual hours of sunlight and solar energy	P15*P24	Negative

Figure 8. The probability mapping of skin cancer occurrences calculated from the model.

Figure 9. The probability mapping of skin cancer occurrences calculated from the model, with the consumption of fruits and vegetables increased by 20%.