**A.1 Multi-distance spatial cluster analysis using Ripley's K function**

See **Figure 12** and **Table 1**.

The observed K value is larger than the expected K value for the first four classes (distance bands): this proves the settlement pattern is more clustered than random at these distance (scale of analysis). Moreover, the observed K value is larger than the upper confidence envelope value (HiConfEnv), thereby proving the spatial clustering for these distance is statistically significant.

#### **Figure 12.**

*Multi-distance spatial cluster analysis (Ripley's K function) [by authors].*


**97**

provided the original work is properly cited.

Koen Olthuis1,2\*, Kasirajan Mahalingam2

2 Waterstudio.NL, Rijswijk, The Netherlands

\*Address all correspondence to: koen@waterstudio.nl

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Flood Resilience Group, UNESCO-IHE, Delft, The Netherlands

, Pierre-Baptiste Tartas2

*Influence of Floods on Spatial Variability of Wetslums Using Geo-information Techniques...*

**A.2 Location and buffer zones of the education and health centers in Korail**

*Health centers and their buffer zone (on the left); education centers and their buffer zone (on the right) [by author].*

*DOI: http://dx.doi.org/10.5772/intechopen.85649*

See **Figure 13**.

**Figure 13.**

**Author details**

and Chris Zevenbergen1

**Table 1.**

*Multi-distance spatial cluster analysis (Ripley's K function) [by authors].*

*Influence of Floods on Spatial Variability of Wetslums Using Geo-information Techniques... DOI: http://dx.doi.org/10.5772/intechopen.85649*
