AIT Online Education Camp 2021 Report

Piyawud Koonmanee 23-Jul-2021 R-Software Hands-on Lecturer: Mr. Ranadheer Mandadi The dependent variable here is the \"rice_totalrevenue\" and the independent variable is the \"rice_income\". When applied the function Pearson's Product Moment Correlation, we get the confidence interval of 0.91 - 0.94, which falls into the strong positive correlation. When applying the Simple Linear Regression function, we get the p-value of < 2.2e-16 which is significantly less than 0.05. Therefore, there is a significant correlation between the variables in the linear regression model of the faithful data set. Additionally, the Multiple R-Squared value is 0.86. This means that, this model explains a lot of variation within the data and is significant. AIT Edu-camp 2021

Piyawud Koonmanee 23-Jul-2021 Similarly, when we plot the Multiple Linear Regression by using \"rice_totalrevenue\" as a dependent variable and \"rice_totalcost\", \"rice_income\", \"rice_laborcost\" as an independent variable. Again, we get p-value of < 2.2e-16 which means that it is statistically significant. The Adjusted R-squared value is 0.99. This indicates that, the changes in the predictors are related to changes in the response variable and that the model explains a lot of the response variability. Lastly, as seen from the graph, the \"rice_laborcost\" does not have an effect to the \"rice_totalrevenue\". Hence, it might be unnecessary to have it. AIT Edu-camp 2021

Sydney Satellite Image 1000.000000 140.000000 340.000000 540.000000 740.000000 940.000000 1000.000000 800.000000 800.000000 600.000000 600.000000 400.000000 400.000000 200.000000 Text 200.000000 .0000000 140.000000 340.000000 540.000000 740.000000 940.000000 .0000000 ÜLegend Made by: Nithiwat Pattrapong Data collected from Google Earth Kilometers Name of satellite used: Landsat 8 0 50 100 200 Date of map formed 26/7/2564 Date of image taken 29/1/2564

Legend ± Noise pollut Schools 94°57'30\"W 94°56'30\"W 94°5 Runways 6°27'30\"N CNEL_65 Arterials 6°27'0\"N Airport_area 6°26'30\"N XShp 6°26'0\"N POPULATION 6°25'30\"N 1364 - 1868 1868 - 2580 6°25'0\"N 2580 - 3496 3496 - 5442 6°24'30\"N 5442 - 7046 6°24'0\"N 6°23'30\"N 6°23'0\"N 6°22'30\"N 6°22'0\"N 6°21'30\"N 6°21'0\"N 6°20'30\"N 6°20'0\"N 6°19'30\"N 6°19'0\"N 4K 012 6°18'30\"N 94°58'0\"W 94°57'0\"W 94°56'0\"

ion for schools nearby Airport areas 55'30\"W 94°54'30\"W 94°53'30\"W 94°52'30\"W 94°51'30\"W 94°50'30\"W 94°49'30\"W 94°48'30\"W 94°47'30\"W 94°46'30\"W 94°45'30\"W 6°27'0\"N Northwestern Prep 6°26'30\"N 6°26'0\"N Elm Elementary 6°25'30\"N 6°25'0\"N Kilometers 6°24'30\"N 6°24'0\"N 6°23'30\"N 6°23'0\"N 6°22'30\"N 6°22'0\"N 6°21'30\"N 6°21'0\"N 6°20'30\"N 6°20'0\"N 6°19'30\"N 6°19'0\"N 6°18'30\"N 6°18'0\"N \"W 94°55'0\"W 94°54'0\"W 94°53'0\"W 94°52'0\"W 94°51'0\"W 94°50'0\"W 94°49'0\"W 94°48'0\"W 94°47'0\"W 94°46'0\"W Made by Nithiwat Pattrapong Date: 26/7/2564 Data provider: AIT Edu-camp 2021

Panramon Kanhasiri (Jelly) R Assignment Multiple Linear Regression Result Summary Here I am making the multiple linear regression model prediction of agricultural income with coefficient of age, years schooling, field area and literacy. The field area has a very significant impact on the agricultural income. Moreover, the summary shows that if the person is literate and has more years of schooling, there would be more income. I speculate that the reason behind this is that the person with more schooling is having more income because they can deal with their assets more effectively though that these two variables aren’t that significant. Finally, the more age, the less income. The person who are younger tends to be more fit and able to work more than the person who is old, this shows that health well-being is affecting the income of an individual also.

KMITL Satellite 200.000000 400.000000 600.000000 800.000000 1000.000000 -600.000000 100.000000 .0000000 -600.000000 100.000000 ² Made by: Joseph Lea 0 55110 220 330 440 Kilometers

e Imagery 2021 800.000000 1500.000000 2200.000000 200.000000 400.000000 600.000000 800.000000 1000.000000 800.000000 1500.000000 2200.000000 .0000000 andre B. Derpo Data Collected From: EarthExplorer.USGS Name of the Satellite used: Landsat 8 Date of the Map formed: 21-07-2021 Date of Image Taken: 27-05-2021

Created by Panramon Kanhasiri ( Jelly ) International School Bangkok - Da 150.000000 350.000000 700.000000 600.000000 500.000000 400.000000 300.000000 0 0.1 0.2150.000000 0.4350.000000

k Sattelite Map ® ata collected from Google Earth Pro - 950.000000 550.000000 750.000000 700.000000 600.000000 500.000000 400.000000 300.000000 0.6550.000000 0.8750.000000 Kilometers 950.000000 1 cm = 45 meters

Noise Pol 745000.000000 2305000.000000 2310000.000000 2315000.000000 2320000.000000 740000.000000 Northwestern Prep : º¹ 735000.000000 730000.000000 Stowe Elem 725000.000000 Elm Elementary º¹ ¹º ¹º ¹º 720000.000000 0 2.5 52305000.000000 102310000.000000 º¹ 152315000.000000 20 K2320000.000000

llution Mapping for Schools 2325000.000000 745000.000000 Legend : 740000.000000 ¹º Schools Runways Arterials CNEL_65 XShpAirport_area POPULATION 735000.000000 3500 - 7046 2500 - 3500 mentary 1700 - 2500 1365 - 1700 730000.000000 1364 - 1365 º¹ 725000.000000 º¹ ¹º º¹ ¹ºKilo2m325e0t0e0.0r00s000 720000.000000 1 cm = 2 km Created by Panramon Kanhasiri ( Jelly )

Pattaratorn Soranathavornkul Hand-on 1: Noise Pollution Mapping for School Hand-on 2: Satellite image In those hand-on, we understand more about map, the relation between the components, and how to make map. We also learn about the different layer of the map. We can create noise pollution mapping for school to demonstrate the risking zone of the pollution. This data consists of data of population, location, and name of school, and other related data. The second hand-on is based on satellite image. I used the image collected by DigitalGlobe’s WorldView-2 satellite at 11:59 a.m. EDT.

R software Hand-on by: Nithiwat Pattrapong Dependant value: crop_totalrevenue Independent values: crop_totalcost, crop_income, field_area By plotting the graph using the data above, we get the p-value less than 2.2e-16 which is less than 0.05 indicating that there is a correlation between the variables.

While the Adjusted R-squared value is 0.9938 which shows that the changes in the independent variables such as crop_income are related to the changes in the dependant variable.

GIS Assignment By Nithiwat Pattrapong i 1. Discuss the data needs and sources for its availability in your country for setting up new “metro rail” in a city. The services that can provide the data we needed to consider building the metro rail are quite rare in Thailand, a developing country. In other to build that, we will need to gather a lot of data by surveying and exploring some specific places. Furthermore, we can collect the data from the satellite as an overview. The data needed to consider for building the metro rail consists of: ▪ Population map Since my metro rail needs customers, so this map is required to maximize the customers. Moreover, we can be able to locate where each station should be so that the customers can use the metro rails conveniently. ▪ Soil map As we have to construct the metro rails that is mostly underground, the soil is necessary to be considered. We need the land where the soil is in good condition so the metro rail will be as strong as possible and avoid other problems related to the soil in the future. ▪ Flood plain area We need to take this kind of area into an account as we need to avoid these areas. And these areas can cause the soil around there become weaker which can lead to unpredictable problems when it comes to underground construction.

▪ Underground map This map is required to have an overview of what pathways should we use for this metro rail so that this metro rail won’t overlap the others. On the other hand, we can use this data to consider and use it as study case that what kind of places or the surroundings is good for building metro rails. ▪ Topographic map Normally the lands are not exactly even, we can deal with uneven lands of Thailand with the help of topographic map which contains shape and character of the earth's surface. ▪ BTS or other kinds of rails pathways Nowadays BTS and others are mostly connected so people can go around the city conveniently. We can take this point into an account to serve the customers and gain more flexibility. ▪ Surveying Since some of the data like what places people mostly use metro rails are not available, we need a team that is responsible for exploring and gathering this kind of data to make the better decision on where should we construct the metro rails. 2. What is Big Data in GIS and data types? Take a case of “smart city” or “intelligent transport system” project and prepare a list and likely sources of data. Big data in GIS, known as Spatial Big Data (SBD), is a field that extracting information from maximum possible sources using established procedures and computational techniques which consists of structured data, unstructured data, and semi-structured data. With the help of this technology, we can be able to improve our transportation such as buses in which nowadays are very unpredictable, sometimes you must wait pointlessly for an hour without knowing when the next bus will arrive. In this case, we can install the sensors which located where those buses are. In addition to those sensors, we can implement this system by collecting the data of the traffic system

that indicate where they have lots of traffic in order to approximate when the buses will arrive at some bus station. Moreover, we can visualize those information at the bus stations so that people know what is the next bus that will arrive soon or where are the buses that you need and when will they arrive. Data such as traffic are able to be collected from Google map. i

Mohamed Arfan Mohamed Nayas Geographic Information Systems Assignment: Q1) Discuss the data needs and sources for its availability in your country for setting up a new “metro rail” in a city. There are many factors and checkboxes that need to be considered before setting up a new metro rail in a city. Strict adherence to rules and regulations placed upon by the government is a must. Standards are needed to be met. The data needed: ● Population map: stations are needed to be built in a highly populated and busy area where a new station would increase productivity. ● Pathways of other different transportation methods: It will be better to have some stations of our metro rail be built near other train stations such as BTS or MRT. This is so that the public can commute easier ● Topographic Map: this data is required because it gives us more details about the land we need to set our metro station in ● Soil map: we have to build our station in areas where problems do not arise due to soil conditions Q2) What is Big Data in GIS and data types? Take a case of a “smart city” or “intelligent transport system” project and prepare a list and likely sources of data. Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value. Sources of data: ● Machine-generated data from Internet-of-Things devices ● Data regarding transportation ● Water management data ● Waste management data: disposal, recycling, recovery

Mohamed Arfan Mohamed Nayas ● Mobile data ● Construction and structural data of buildings ● Air quality and pollution data

Piyawud Koonmanee 15-July-2021 GIS - Geographic Information System Lecturer: Prof. Nitin Kumar Tripathi 1. Discuss the data needs and sources for it’ s availability in your country for setting up new “metro rail” in a city. In Thailand, a developing country, most of the data is not available to us online, but the government is trying to make it public. When building a metro rail in a city, something rather complicated since you will need a lot of data and information about that specific place. First, the basic 2D and 3D mapping of that geographical location are required to estimate the length and location of the metro railway. This also included parcels and road mapping. GISTDA and Google are working together to make these maps available to the public, yet, the government is censoring some areas for stability matters such as military bases. Meaning, the metro construction company has to request the government for the uncensored version if needed. Second, the land use map is also necessary to look at the population and building density. It would be unprofitable for the metro company if they decided to build it in an area with a small population that has few traffic jam problems. They will also have to seek the linkage building between the ground and underground level. This type of map is available for public use and can be found easily. Third, the topographic map that concerns the shape and character of the Earth's surface is also necessary. Knowingly, Thailand is mostly a plain region, but the company has to take an account of the uneven region too. The metro has to be at the appropriate height so that in some areas it is not too deep and not too shallow. Again, this type of information is also available for public use, but the detailed mapping is not fully available in some regions. AIT Edu-Innovation Camp 2021

Piyawud Koonmanee 15-July-2021 Fourth, the floodplains area is also a must. Upon the heavy rain, it might flood the metro station and make it unusable. When this is not taken into an account it will highly affect the metro and might destroy the facilities and major components which can come at a hefty price tag. It is better to find a suitable location, as well as an excellent drainage system. This type of map is not fully available online but can be requested from the government. Fifth, detailed underground mapping is required. This includes everything that can be seen underground such as the electrical wiring, drainage and piping system, water and waste tank and underground level of the building. They need to make sure that building the new metro won't affect the residential and commercial buildings. This type of map cannot be found online due to stability and private matter. The construction company should request it from the government. Sixth, the soils data is highly important. A suitable type of soil that is sand or clay can make building the metro easier and faster. In Thailand, most of the region is comprises clayey and loamy soft soils. They are high water content, high compressibility, low shear strength and low permeability. These activities generate stress changes on the soil at the front and around the tunnel, so the existing cracks could reopen again, which is extremely unsafe. This type of map can be found online and is rarely changed throughout the decade. However, a detailed and fresh map can be requested from the government. Last, the important of all, the survey from the population in that city. The metro company has to survey the people if they think it is necessary and suitable for that location or not. Moreover, they must also research and make sure that the noise and vibration from the metro don't affect the surrounding environment and won't disturb the population. These are some of the few data that is required to build a single metro station. It requires thorough information from each sector and normally takes over 3 years of planning, which depends on the availability and freshness of the data. It would be great if the government has some of these types of data available and make it open AIT Edu-Innovation Camp 2021

Piyawud Koonmanee 15-July-2021 source so that everyone can contribute and keep the data fresh and access it freely (maybe censor some of the private areas from the public). 2. What is Big Data in GIS and data types? Take a case of “smart city” or “intelligent transport system” project and prepare a list and likely sources of data. Big data in GIS or Spatial Big Data (SBD) is a field that treats ways to analyze the systematically extract information from the datasets that are large and complex to enhance the accuracy of the mapping and similar techniques. Those data are consisting of: • Structured data – indices, raster and vector map, TIN, tabular • Semi-structured data – XML documents, NoSQL database • Unstructured data – scientific data, Google Earth, Radar and Sonar, data streamed from social media, spatial logs, etc. With these types of data, it can make the city smarter and become more efficient. For example, the traffic system. Normally it would work on the timing that has been pre-calculated. However, at some point in time, the driver has to wait for the red light when there's no traffic. This could be implemented by attaching a camera at the intersection and control the light color. It can be by using machine learning to accurately point out an amount of the incoming cars from a group of pre-trained car images and AI to calculate the speed and duration of the light color. The data can also come from Google Map that is using GPS in the background of the smartphone to count the number of the driver in that area and make a traffic map for public use. AIT Edu-Innovation Camp 2021

Noise Pol 745000.000000 2305000.000000 2310000.000000 2315000.000000 2320000.000000 740000.000000 Northwestern Prep : º¹ 735000.000000 730000.000000 Stowe Elem 725000.000000 Elm Elementary º¹ ¹º ¹º ¹º 720000.000000 0 2.5 52305000.000000 102310000.000000 º¹ 152315000.000000 20 K2320000.000000

llution Mapping for Schools 2325000.000000 745000.000000 Legend : 740000.000000 ¹º Schools Runways Arterials CNEL_65 XShpAirport_area POPULATION 735000.000000 3500 - 7046 2500 - 3500 mentary 1700 - 2500 1365 - 1700 730000.000000 1364 - 1365 º¹ 725000.000000 º¹ ¹º º¹ ¹ºKilo2m325e0t0e0.0r00s000 720000.000000 1 cm = 2 km Created by Panramon Kanhasiri ( Jelly )





























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