The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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subjects is straightforward. Thanks to the excellent visual 
quality of the image and the effectivity of displaying the 
satellite image even at 1:2000 scale. An open source GIS 
software called fGIS was first used to delineate the areas 
occupied and controlled by the informal settlements. The choice 
of the software was mainly due to its availability at the time of 
mapping. 
One particular challenge in mapping informal settlements is the 
existence of houses partially or totally under large trees. 
Significant portion of the campus is vegetated particularly the 
northwestern part called the “arboretum” which is considered to 
be the last remaining forest in the city. Mapping informal 
settlements in this situation required some innovations 
involving actual field boundary demarcation and the aid of 
positioning devices to plot the peripheries of the community. A 
handheld Garmin Global Positioning System (GPS) attached to 
the pocket computer provided instantaneous positioning 
information and markings on the satellite image which was 
useful not only in documentation of the paths that were taken by 
the field crew but also as a navigational aid. 
Fieldworks ensued to validate and update the mapping result 
with the aid of GPS. Stratified random sampling was employed 
to verify samples from all informal settlement clusters. A high 
95% identification accuracy was achieved. The 
misidentification is attributed to the one year temporal 
difference of the data and the actual fieldwork, during which 
eviction occured as a result of the continuous effort of the 
campus administration to recover the control of the land. Base 
from the QuickBird satellite image, around 16% of the total 493 
hectares UP Campus or roughly 79 hectares may be labeled as 
informal settlements. This 2004 UP Campus informal 
settlements map will now serve as the baseline data for 
monitoring further encroachment. 
After the establishment of the UP Campus informal settlements 
map, the individual houses were digitized to prepare for the 
other phase of this research which is to estimate the population 
of informal settlement communities. In the high resolution 
satellite image, the semi-formal houses with an average surface 
area of 30 square meters are large enough to be easily 
delineated. Problem is apparent only in mapping the roofs of 
each slum type houses. The very cramped area even as small as 
an unimaginable 10 square meters is very difficult to visually 
delineate especially when they are placed so closed with each 
other forming a seemingly one large continuous roof. Jain (2008) 
experienced the same in extracting information in old 
developments as well as in informal settlements where dwelling 
size is considerably small and building are placed adjacent to 
each other. 
6. RESULT AND ANALYSIS 
6.1 Regression Analysis 
A fieldwork was conducted to gather sample data for the 
regression analysis. A stratified random sampling method was 
employed to assure complete representation of the total 
population throughout the image. A total of 160 samples were 
collected bearing the identity number, number of residents in 
each house, informal settlement category whether slum or semi- 
formal, roof-derived surface area of the house, and the type 
indicating whether single or multilevel. The field data gathering 
is a challenging task. There is security to consider and there is 
the hesitation of residents to share informations for fear that 
anything they say might be used against them. 
The sample data was processed using the Grid and Theme 
Regression, a program made by Jeff Jenness in the AVENUE 
programming language, an extension of the Arc View 3.2 
software. The initial plot of the raw data in Figure 3 showed low 
correlation of the two variables in all polynomial order with the 
third order producing the highest. The summary of correlation 
is shown in Table 1. This may be attributed to the undivided 
type of data (slum and semi-formal) and the existence of 
multilevel houses showing large number of residents with very 
small area, producing an inconsistent trend or random 
occurrence. 
Scatterplot is linked w rth table 'fagdata 1 _regress_1 .dbf... 
Selecting features from one will automatically select features from the other. 
Model = B0 + B 1 ’'[Area] 
R-Squared = 0.0007, Adjusted R-Squared = -0.0057 
Figure 3. First Order Regression Plot 
1 st Order 
2 nd Order 
3 rd Order 
R 2 
0.000689 
0.002756 
0.013938 
Table 1. Summary of Correlation 
Having multilevel floors among the houses appears to be the 
biggest problem in this endeavor to establish an effective way of 
estimating population. This exposes the limitations of the 
satellite image being not able to recognize this particular 
natural phenomena. However, this should not hinder us from 
formulating solutions to discover and make something useful. 
According to Rindfuss and Stem (1998), each data source has 
its imperfections, but combining sources with different 
limitations might provide a better picture of the entire 
phenomenon. In this way, remote sensing even with its 
imperfections, can make a contribution to social scientific 
measurements by improving on some measures and cross 
checking others. 
Modifying the data entries by removing the multilevel houses 
and separating the sample data of the two informal settlement 
types, the number of samples was reduced from 160 to over a 
hundred. For the two informal settlement types, slums and semi- 
formal, the same processing using the Grid and Theme 
Regression in AVENUE has been repeated. The correlation of 
the variables for both type has dramatically increased in all 
polynomial order with the third order producing the