Table 1 
Housing Stock by Type of Housing Projects in BMA 1974 and 1984 
Number 
of Units 
Percent 
Type of housing project 
1974 
1984 
1974 
1984 
1. 
Shophouse project 
3,684 
10,798 
0,83 
1,30 
2. 
Private housing proj. 
18,686 
122,487 
4,24 
14,88 
3. 
Land-Subdivision proj 
. 18,835 
46,388 
4,27 
5,64 
4. 
Individual buildings 
242,351 
353,860 
54,93 
42,99 
5. 
Klong houses 
21,868 
21,592 
4,96 
2,62 
6. 
Public Housing proj. 
- NHA 
9,377 
47,882 
2,13 
5,82 
- non NHA 
not available 
26,826 
— 
3,26 
7. 
Slum and squatter 
type settlements 
104,323 
152,211 
23,64 
18,5 
8. 
Others 
22,060 
41,160 
5,00 
5,00 
Total 
441,194 
823,204 
100 
100 
Table 2 Housing Stock by Type of Housing Units 
in BMA 1974 and 
1984 
Number 
of Units 
Percent 
Type of Housing Unit 
1974 
1984 
1974 
1984 
1. Shop houses 
134,436 
244,945 
40,47 
29,76 
2. Detached houses 
165,327 
263,626 
37,47 
32,02 
3. Semi-detached houses 
588 
5,503 
0,13 
0,67 
4. Terraced houses or Town 
houses or row houses 
8,208 
44,896 
1,86 
5,45 
5. Multi-storey apartments 
6,250 
70,863 
1,43 
8,61 
6. Slum houses 
104,325 
152,211 
23,64 
18,49 
Total 
441,194 
823,204 
100 
100 
Source: 1974 & 1984 airphoto Interpretation, NHA. 
where housing densities were applied. In the 
previous study, counting houses was generally 
only done in sampling areas to derive the charac 
teristic of the housing density. 
5. Classification system of residential areas was 
designed for particular purpose and interpreta 
tion was done by local staff who required 6 man- 
months to complete the job. Interpretation cost 
accounted for 50* of the cost of a set of en 
larged SFAP. 
6. Enlarged SFAP to a scale 1:15,000 was found to be 
of sufficient quality for counting individual 
houses, further photo enlargement is required only 
in the informal settlement area. 
Although the main emphasis in this survey has been 
on the application for the housing sector study, the 
photographs have also been widely used by many other 
local institutions for various other applications 
like map updating, land use inventory, site selec 
tion and infrastructure planning. 
No data acquisition through aerial photography is 
cheap but SFAP revealed that it is justifiable, it 
is an approach to data acquisition that could be 
used elsewhere in the world where time and budget 
are a primary constraint. Particularly in the near 
future where high resolution satellite images are 
delivered timely for an overview in change detec 
tions, SFAP will be of great value in supplementing 
detailed information of particularly areas of inter 
est where large format may not be feasible. 
ACKNOWLEDGEMENT: 
The authors are indepted to Prof. Dr. M. Juppenlatz, 
Ir. S.A. Hempenius and Ir. C.A. de Bruijn for their 
help and valuable suggestions for carrying out both 
the BMR housing sector study and writing this paper. 
They also like to express their appreciation to Drs. 
V.F. Polle and Drs. P. Hofstee for their creative 
input in the preparation of this manuscript. 
REFERENCES 
Adeniyi, P.0. (1983). "An Aerial Photographic Method 
for Estimating Urban Population". Photogrammetric 
Engineering and Remote Sensing, Vol. 49, No. 4, 
April, pp.545-560 
Boapeah, S.N. and TIPPLE, A.G. (1983). "Estimating 
Housing Stock in a Third World City, a Method used 
in Kumasi." Third World Planning Review, Vol. 5, 
No.2, May, pp.177-188. 
Curran, P.J. (1981) ."Remote Sensing: The Role of 
Small Format Light Aircraft Photography". 
Geographical papers, Department of Geography, 
University of Reading. 
Hofstee, P. ed. (1985). "Small Format Aerial 
Photography Applications". ITC-Urban Survey 
Juppenlatz, M., Leekbhai & Chanond eds. (1985). "An 
Innovation in Aerial Photography for Development 
Planning". Bangkok, July