Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap

Figures & data

Figure 1. The geographical extent of Baden-Württemberg state of Germany as the study site (left), CORINE land cover map of the study area (right).

Table 1. Dependent and independent variables used in the logistic regression model.

Variables	Acronym	Denotation	Date of collection	Data range
Dependent	Hot spots	Highly contributed areas	31 December 2012	–
Independent	FP	Percent of foreigners	2011	5–24%
	In	Annual income	2009	17,613–31,020 €/year
	OS	Overnight stays	2011	205,212–16,032,421
	Tx	Paid tax on the income	2007	288,941–2,371,225 €/year
	PD	Population density	2011	102–2958.2 persons/km^2
	SW	Social welfare	2004	1384–23,517
	UR	Unemployment rate	2012	2.6–7.7%
	BHS	People with education level below high school	2011	12.2–25.5%
	PT	People with practical training	2011	49.3–66.7%
	EP	Educated people	2011	4.9–22.3%
	AG1	Age group 1: below 18 years old	2011	13–19%
	AG2	Age group 2: 18–50 years old	2011	38–53%
	AG3	Age group 3: 50–70 years old	2011	21–26%
	AG4	Age group 4: >70 years old	2011	13–22%
	DtB	Distance to built-up areas	2006	–

Figure 2. Independent and dependent variables in the LRM.

Figure 3. Hot spot analysis of nodes based on their quantity per cell (a) and kernel density analysis of nodes (b).

Figure 4. The probable spatial distribution of future contributions in OSM based on LRM.