VALIDASI PREDIKSI PERJALANAN DARI METODE PENGINDERAAN JAUH (REMOTE SENSING) DI KOTA MAKASSAR
The use of remote sensing in the statistical modeling approach began in the mid-1950s, particularly with the aim of making an alternative to the population census. In the transportation planning, the use of spatial information that is indispensable in the interaction of land use and transportation....
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
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[Yogyakarta] : Universitas Gadjah Mada
2012
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| Online Access: | https://repository.ugm.ac.id/98738/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=54658 |
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| Institution: | Universitas Gadjah Mada |
| Summary: | The use of remote sensing in the statistical modeling approach began in the mid-1950s, particularly with the aim of making an alternative to the population census. In the transportation planning, the use of spatial information that is indispensable in the interaction of land use and transportation. Land use are important factors that affect the movement and activities, as the trip generation. As stated by Salter and Hounsell, (1996), information obtained from the land use for the purpose of the trip generation process is the density of residential development as well as the conditions and types of houses.
The purpose of this study to assess the extent of the accuracy in predicting the attraction trip by creating a model based on land use, as well as giving a correction factor. Variable used is variable according to the results of satellite imagery that is building area. Location of the study was based remote sensing are processed by a doctoral student of Geography is located in Makassar City in six districts. Primary data collection using interviews to the attraction trip locations. Development model using regression analysis, taking account of the regression model, whether in the form of linear or non-linear.
From the analysis of the model is tends to form a linear regression with a test of linearity and the Curve Estimation with ANOVA Significance of Linearity <0.05 and the value of Sum of Squares for Linearity is greater than Deviation from Lineariy parameter. so the trip attraction model has the formula: Y = 117 + 0.077.X with R Square 0.948 and Adj. R Square 0.948. From the results of the t-test is also no difference between the model predictions and observations with a value of significance (P) 0.999 (Sig.> 0.05). Predictions from the model then be built a trip origin-destination matrix, which was previously performed reverse projection/Backcasting to the year 2007, that can be calibrated to primary data matrix origin-destination of 2007. Of the calibration process will get a correction factor (Fcr) for Model PJ, with the new formula is Y = 1821 + 0.841.X, where X is the trip of the Model PJ (Ypj). Corrections were produced from the mean absolute percentage error (MAPE) is 1.31% from 1.39% previously value. The final step is to test the data a building area of remote sensing results, results obtained from via the t-test is no difference with the Sig. (P) 0.320> 0.05. |
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