Weighting factor determination

The crucial part of the dasymetric model is the determination of the weighting factors that distribute the population to land use classes during the day- and night-time. This research analyzes the characteristics of socio-economic data of the study area in order to determine people’s activities in the land use class and

to derive the weighting factors thereof. There are two steps of weighting factor determination:

- Derivation of the potential number of people engaged in different land use activities by occupation data. It is a knowledge-based initial percentage value for describing where people are located. Furthermore, this value can be used for calculating the percentage of people engaged in land use activity for every village.

- The generalization of the weighting factor. This analysis ensures that weighting factors are specific to every village wherever necessary, or generalized by the village categories such as urban, rural, coastal and non-coastal.

To this end, socio-economic data (potential of village and occupation) are required.

Potential number of people engaged in different land use activities

Available methods developed to provide weighting factors to land use classes do not consider the behavior of people based on their activities. An improvement is needed here to provide an appropriate and objective assessment of the weighting factors to derive information as precisely as possible about people at risk. Therefore, statistical data of people’s activities in certain administrative units (villages) are analysed to estimate the weighting factors.

There are two sources of statistical data in Indonesia that provide information about people’s activities at the village level and that can be used for the derivation of weighting factors: potential of village data (PODES) and the census data. The PODES data set contains information on the main income sources of the population in a community and the number of workers and non- workers. Additionally, the census data provide information on the percentage of employment in different sectors in each community. These parameters provide an indication of the type, volume and locality of human activities, and can be used to calculate the potential number of people engaged in different land use

Based on this assumption of potential number of people engaged in different land use activities by occupation data, the calculation of where are people located in the different land use can be done in every village. The general weighting factor is then generated, as described below.

Figure 4.7 Potential number of people engaged in different land use activities during the day- and night-times in the village

Generalization of weighting factor

The first step of weighting factor generalization should answer the question “Could the weighting factors be generalized for the whole coastal area of Indonesia, or are individual weighting factors needed due to different village characteristics?” An analysis of the occupation data answers this question. There are at least 9100 villages with different characteristics in coastal Sumatra, Java and Bali.

There are four possibilities to differentiate and regionally villages category according to their characteristics, as follows:

- urban coastal, urban non-coastal, rural coastal and rural non-coastal areas;

- island (Sumatera, Java, and Bali) plus the first mentioned characteristics;

- potential economics of village; - municipalities.

The four groups of differentiation are statistically analyzed and the decision on the weighting generalization can be made accordingly. The statistical analyses of the differentiation groups are based on standard deviation and coefficient of variation.

This analysis clarifies if the general weighting factors can be transferred to other areas or not. If it is possible, the uncertainty analysis with regard to the standard deviation of that general weighting factor can be applied.

4.3.3. Accuracy assessment

The accuracy assessment needs reference data of the true population distribution. To this end, a questionnaire was developed and distributed in the study area of Cilacap District. The main questions were:

 What is the type of working sector?

 During which time of the day do the inhabitants usually work?

 In what kinds of land use, do they usually work or stay during their activities?

 Where are these typical sites located: outside or inside their village, how far away?

Through this questionnaire, crucial information about the amount of people in an occupation sector who are working or carrying out their activities in a certain land use during day- or night-time was assessed. Additionally, information on

))

(

.(

.

1 , O I d RQ n j i ij ij ij

W

X

P

P

S

S

RQ

P









 (4.18) Where:

RQ

P

_{ij is the number of people in polygon j in land use i in real condition} PI is the number of inbound commuters

PO is number of outbound commuters RQ

W

is the weighting factor derived from questionnaire.

The error of population distribution in the model can be calculated as follows:

n

P

P

RMSE



ij



ijRQ 2

)

(

(4.19)

P

RMSE

COV



(4.20)

%

100

(%)

x

P

P

P

E

ijRQ ijRQ ij PD





(4.21) Where:

RMSE

is the root mean square error

COV

is the coefficient of variation

P

is the average population distribution

PD

E

is the error of population distribution

ij

P

is the number of people in polygon land use (model)

ijRQ

P

_{is the number of people in polygon land use (questionnaire).}

In document Khomarudin, Muhammad Rokhis (2010): Tsunami Risk and Vulnerability: Remote Sensing and GIS Approaches for Surface Roughness Determination, Settlement Mapping and Population Distribution Modeling. Dissertation, LMU München: Fakultät für Geowissens (Page 102-106)