Jun 14
แหล่งข้อมูลสำหรับ AEC หรือ ASEAN Economic Community
May 31
Tourism is an important sector in the global economy. In many countries, tourism is the main source of revenue, Thailand is one of them. However, tourism sector is a fast moving sector. It is very sensitive to various factors and also vulnerable. The tourism markets for each destination (country) are also very diverse. Tourism data are available and updated frequently. One of the most important report of national tourism statistics; number of tourist arrivals from each country of origin, their average length of stay and total receipt or expenditure. These tourism statistics are important but often reported separately due to the limitation of software used by analysts.
The following graph represents profile of international tourists in Thailand in 2005.
The picture above was produced in R with package ‘ggplot2′ using the code below.
# Step 1: Import data into R
exp05 <- read.csv("http://dl.dropbox.com/u/46344142/thai_tour_2005.csv", head = T)
# Step 2: Load 'ggplot2' package for plotting elegent data visualisation
library(ggplot2)
# Step 3: Specify x and y axis, label, size of the bubbles and colour of the region
exp <- ggplot(exp05, aes(x=number, y=length, label=country, size=receipt, colour = region))
# Step 4: Create a plot and add texts to x and y axis
exp + geom_point() + geom_text(hjust=0.7, vjust=2) + labs(x = "Number of Tourist Arrivals", y = "Length of Stay (days)") + scale_area("Receipt (M. USD)") + scale_colour_hue("Region")
Last year Thailand faced the worst floods in their history. The World Bank (2011) reported that the 2011 Thailand floods were “The biggest damages and losses were in the manufacturing sector, with a total of THB 1,007 Bn (USD 32 Bn approximately)”. The tourism and agricultural sector were also affected and losses approximately THB 95 Bn (USD 3 Bn) and THB 40 Bn (USD 1.3 Bn) respectively (The World bank, 2011).
Therefore, this study aims to evaluate the performance of organisations that took actions in emergency relief for this disaster. We measure performance based on their preparedness for such disaster as well as how they responded to the events. Data collected via GoogleDoc from 382 respondents (victims) were (A) explored and the (B) used to fit with the conceptual model of performance measurement for emergency relief logistics.
Moving away from the basic barplot instantly provided by Google Doc, we can do a better job to visualise what people rate the performance of each organisation in terms of preparedness and response to the floods. An we can do so via R. Followings are what I produced in R.
1. Level Preparedness
# Boxplot preparedness performance k <- ggplot(flood, aes(factor(org), aPRE)) k + geom_jitter(aes(colour = org), size = 4) + opts(legend.title = theme_text(size = 20, face = "bold"), legend.text = theme_text(size = 10)) + opts(title = "Level of Perceived Preparedness of each Organisation") + opts(plot.title = theme_text(size = 15, face="bold", colour = "blue"))
2. Level of Responses
# Boxplot response perforance c <- ggplot(flood, aes(factor(org), aRES)) c + geom_jitter(aes(colour = org), size = 4) + opts(legend.title = theme_text(size = 20, face = "bold"), legend.text = theme_text(size = 10)) + opts(title = "Level of Perceived Response of each Organisation") + opts(plot.title = theme_text(size = 15, face="bold", colour = "blue")) <pre>
Summary
Albeit emergency relief logistics is an emerging field in operations, logistics and supply chain management, the development of performance measurement is still limited. Although one of the objectives of emergency relief logistics is to satisfy customers (victims in the disaster) the development of performance measurement based on victim’s perspective is limited. Then this study propose a measurement of emergency relief logistics performance and tested with an empirical data from the Thailand floods in 2011. We fit the propose measurement model with the data of 382 respondents using Confirmatory Factor Analysis with Mplus version 6 and R version 2.14.1. The result shows that the model is fit with the data. It was found that response had the highest contribution to the total performance, followed by preparedness and recovery respectively. The result also shows that information, operations and evacuation have different contribution to the performance of each stage.
Keywords: Humanitarian logistics, Emergency relief, disaster, floods, Thailand
Download: pdf
R code used in the paper
</pre> library(lavaan) flood1.cfa <-' PRE =~ x411 + x412 + x413 + x414 + x415 + x416 + x421 + x422 + x423 + x424 + x425 + x431 + x432 + x433 + x434 RES =~ x511 + x512 + x513 + x514 + x515 + x516 + x521 + x522 + x523 + x524 + x525 + x526 + x527 + x531 + x532 + x533 + x534 + x536 REC =~ x611 + x612 + x613 + x614 + x615 + x616 + x617 + x621 + x622 + x623 + x624 + x625 + x626 + x627 + x631 + x632 + x633 + x634 + x636 PEF =~ PRE + RES + REC ' fitFlood1 <- cfa(flood1.cfa, data=flooddata) summary(fitFlood1, standardized=TRUE, fit.measures=TRUE) <pre>
นายกอภิสิทธิ์แถลงส่งมอบงานบริหารประเทศในเรื่องต่างๆ ทั้งเศรษฐกิจ การเมืองและสังคม
ถือได้ว่าเป็นการแถลงผลงานของประเทศทางอ้อม รวมทั้งป้องกันรัฐบาลใหม่มาอ้างว่ารัฐบาลเก่าทิ้งปัญหาไว้มากมาย
การแถลงแบบนี้ก็ดีนะครับเพราะประชาชนก็จะได้ฟังความจากทั้งสองฝ่าย อย่างไรก็ตามสิ่งที่ผมเองอยากเห็นมากกว่าคือการตั้งคณะกรรมการร่วมระหว่างประชาธิปัตย์และเพื่อไทยในการส่งต่องานจริงๆ อย่างที่ทำกันในประเทศชั้นนำ เช่น สมัย Obama รับต่อจาก Bush ใน USA หรือ David Cameron ที่รับต่อจาก Gordon Brown ที่ UK
ที่นี้ต้องรอดูว่านายกหญิงคนแรกจะแถลงเข้ารับตำแหน่งอย่างไรบ้าง
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