Difference between revisions of "Group 3 Overview"
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Geospatial analysis was developed for problems in the environmental and life sciences, which has currently extended to almost all industries including defence, utilities, social sciences, and public safety. The application of geo-visualization using geographically weighted regression (GWR) is an exploratory technique mainly intended to indicate where non-stationarity is taking place on the map. It is a good exploratory analytical tool which creates a set of location based parameter estimates, able to be mapped and analysed to give spatial information for the relationship of explanatory variables and response variable. | Geospatial analysis was developed for problems in the environmental and life sciences, which has currently extended to almost all industries including defence, utilities, social sciences, and public safety. The application of geo-visualization using geographically weighted regression (GWR) is an exploratory technique mainly intended to indicate where non-stationarity is taking place on the map. It is a good exploratory analytical tool which creates a set of location based parameter estimates, able to be mapped and analysed to give spatial information for the relationship of explanatory variables and response variable. | ||
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Our study uses environmental data to explore air pollution impact in northern region of China. The project scope covers the analysis, model and visual representation of multivariate factors like wasted air emission, investment of air treatment, GDP in secondary industry, area of gardens and green, No. of motor vehicles, which leads to the air pollution in each city area of the province or municipality with the assistance of interactive charts and graphs. | Our study uses environmental data to explore air pollution impact in northern region of China. The project scope covers the analysis, model and visual representation of multivariate factors like wasted air emission, investment of air treatment, GDP in secondary industry, area of gardens and green, No. of motor vehicles, which leads to the air pollution in each city area of the province or municipality with the assistance of interactive charts and graphs. | ||
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=Data Source and Preparation= | =Data Source and Preparation= | ||
| − | The following data sources | + | The following data sources are used for the project: <br> |
1. Air pollution data from National Bureau of Statistics of China between 2011 and 2015.<br> | 1. Air pollution data from National Bureau of Statistics of China between 2011 and 2015.<br> | ||
2. Air pollution data from online air quality monitoring platform between 2011 and 2015. | 2. Air pollution data from online air quality monitoring platform between 2011 and 2015. | ||
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=Approach= | =Approach= | ||
| − | The main framework used for the application is RShiny and Leaflets. There are some packages in R to build a smooth interactive interface for extracting, exploring, and modelling the data. We | + | The main framework used for the application is RShiny and Leaflets. There are some packages in R to build a smooth interactive interface for extracting, exploring, and modelling the data. We implemented GWR model and parallel coordinate graph for spatial analysis, which enables us to study the impact and influence of various factors contributes to the air pollution situation. The interactive graphs and charts has also been created to allow the user to apply different scenarios and make deep exploration. |
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<br> | <br> | ||
=Selection of Tools= | =Selection of Tools= | ||
| − | Since this course aims at pursuing the good command of R language, for the display part we will choose R to implement our visualization. To be more precise, we | + | Since this course aims at pursuing the good command of R language, for the display part we will choose R to implement our visualization. To be more precise, we use R Markdown and the other relevant package like gwr, ggplot2,ggraph, and JavaScript library like d3.js, leftlet.js. At the data preparation step, we use JMP Pro and Tableau as well. |
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Revision as of 01:58, 29 November 2017
| PROPOSAL | POSTER | APPLICATION | REPORT |
Contents
Abstraction
Geospatial analysis was developed for problems in the environmental and life sciences, which has currently extended to almost all industries including defence, utilities, social sciences, and public safety. The application of geo-visualization using geographically weighted regression (GWR) is an exploratory technique mainly intended to indicate where non-stationarity is taking place on the map. It is a good exploratory analytical tool which creates a set of location based parameter estimates, able to be mapped and analysed to give spatial information for the relationship of explanatory variables and response variable.
Our study uses environmental data to explore air pollution impact in northern region of China. The project scope covers the analysis, model and visual representation of multivariate factors like wasted air emission, investment of air treatment, GDP in secondary industry, area of gardens and green, No. of motor vehicles, which leads to the air pollution in each city area of the province or municipality with the assistance of interactive charts and graphs.
Motivation
Conventional linear regression models are commonly used to analyse environmental problems to see the influences and basic relationship of factors which contributes to the pollution. We feel that a geographical regression model can also be built to make spatial statistical analysis for the issues related to environment. GWR is a technique by allowing local instead of global parameters to be estimated. The model fits where a localized adjustment conveys a more meaningful message with involvement of spatial areas. It uses a moving window weighting mechanism for localised models detected at target location. Results are mapped to an interactive exploratory geo-application with the consideration of the nature of data spatial heterogeneity.
Objective
Our objective is to provide an interactive and exploratory geo-visual analytics tool for regional and urban planner and policy maker to visualize, analyse and model the location-based data.
Data Source and Preparation
The following data sources are used for the project:
1. Air pollution data from National Bureau of Statistics of China between 2011 and 2015.
2. Air pollution data from online air quality monitoring platform between 2011 and 2015.
Variable ,Full Name ,Unit
City ,City Name ,-
Province_Municipality ,Province or Municipality Name ,-
AQI ,Air Quality Index ,-
IPT ,Investment of Pollution Treatment ,RMB (million)
IWAE ,Industial Waste Air Emission(Sulphur Dioxide) ,Ton thousand
RISWU ,Ratio of Industrial Solid Waste Utilized ,%
TRLW ,Treatment Rate of Living Waste ,%
GDPSI ,GDP of Secondary Industry ,RMB (million)
NFTWA ,No of Facility for Treatment of Waste Air ,Set
GIO ,Gross Industrial Output Value ,RMB (million)
AGG ,Area of Garden & Green ,ha
NMV ,No of Motor Vehicle ,Unit thousand
Approach
The main framework used for the application is RShiny and Leaflets. There are some packages in R to build a smooth interactive interface for extracting, exploring, and modelling the data. We implemented GWR model and parallel coordinate graph for spatial analysis, which enables us to study the impact and influence of various factors contributes to the air pollution situation. The interactive graphs and charts has also been created to allow the user to apply different scenarios and make deep exploration.
Selection of Tools
Since this course aims at pursuing the good command of R language, for the display part we will choose R to implement our visualization. To be more precise, we use R Markdown and the other relevant package like gwr, ggplot2,ggraph, and JavaScript library like d3.js, leftlet.js. At the data preparation step, we use JMP Pro and Tableau as well.
Challenge
1.The preparation of dataset to fulfil the minimum model requirement
2.The interaction of R graphs and maps with Shiny.
3.The building of an effective and accurate GRW model in R.
4.Find the most suitable tools and libraries to implement the visual features.
Reference
[1] https://rstudio-pubs-static.s3.amazonaws.com/44975_0342ec49f925426fa16ebcdc28210118.html
[2] https://www.rdocumentation.org/packages/spgwr/versions/0.6-32/topics/gwr
[3] https://cran.r-project.org/web/packages/spgwr/vignettes/GWR.pdf
[4] http://csiss.org/GISPopSci/workshops/2011/PSU/readings/Grose-Brunsdon-Harris-GWR.pdf
