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- 1. Decision Management Systems Title Instructor: Student: Contact: Date: Introduction Despite being a rising like star in the global economy, China still has many issues needing to be addressed such as political freedom, health care incompetence, environment crisis, etc. Smog is a huge concern in recent years. It is both health-related and environment-related. On December 8, 2015, Beijing's city government issued its first red alert for pollution. They closed schools and construction sites and restricted the number of cars on the road. Ten days later, the second red alert was issued as the air pollution continued to chock the city sky. The smog problem is not only in Beijing but also has spread over other major cities across the country. The data set in my study is combined from three data sets (Table 1, 2 & 3) that are retrieved from the National Bureau of Statistics of China, which ensures the quality of the source and data itself. There are no missing values or bad records in the data set. The primary data sets are Ambient Air Quality in Major Cities, Main Pollutant Emission in Waste Gas in Main Cities and Pollutant Emission in Waste Water in Main Cities. There are a total of 21 variables in the final data set. City name
- 2. serves as index and year indicates the data collection period. The targeted variable is “Days of Air Quality equal or above Grade II”. Other variables are interests as well such as, “Volume of Consumption Soot Emission”, “Annual Average Concentration of PM10”, etc. First section of data is quantitative data of chemical concentrations and the Air quality. Second section is the measurements of the emission of gas waste and third section is the records of emission of water waste. The emission of water waste might seem little related to the air quality, but it is part of the environment pollution elements. It would be useful when we explore the data in Watson Analytics to confirm that. Data exploration To discover all the meanings of the data variables, data exploration of Watson Analytics not only covers all data aspects but also visualizes them. Some suggested questions are good starting point. The first question examined the volume of consumption soot emission per city. Data visualization (Exploration Figure 1) shows that the City of Harbin has the highest volume of this emission then followed by the capital of China, Beijing. Nanjing is the lowest and similar to cities of Kunming and Fuzhou. Next question provided the visualization of the living emissions of the cities (Exploration Figure 2). The cities of Shanghai, Chongqing and Guangzhou are the top three. According to the report from the United Nations, these three cities are among the top five most populated cities in China between the years of 2010 – 2015. Therefore, the data evidently shows the high living emissions. Now, we would like to explore the details of Days of Air Quality equal or above Grade II compared by cities (Exploration Figure 3). We can see that Shijiazhuang occupied the smallest area, which means that this city has the least days of good air quality. Shijiazhuang is a heavy industry concentrated area, which explains the low air quality. Meanwhile, Haikou and
- 3. Kunming showed that they have the best quality for air. It is very obvious that both cities have a low range of the living emission. It is not hard to be convinced that since the city of Haikou and Kunming both are not highly populated. In addition both are located far away from the heavy industrial area. To further explore the data, we would like to apply the features available to discover more details of the data. To focus on the Days of Air Quality ≥ Grade II, we will create a new variable called “Region” to group the cities in four group by their location. Since the north showed the highest Volume of Consumption Soot Emission and South is just opposite (Exploration Figure 4.) so we would like to see all other differences in comparison while we apply more filters. As Region is applied as a global filter (Exploration Figure 5), South and North in particular. We can see that the cities in southern region all have more days of better air quality than northern ones. After we calculated the total of emission in waste gas, it had a similar outcome – all the southern cities have less industrial total waste in gas compared with cities in north. If we look closely by applied additional filters - Annual Average Concentration of PM10 < 101, which means air is not polluted. These five cities are all in southern region. The data exploration is really helpful in finding relationships between variables. It could be used in many cases. In my field, research, principle investigators could use the features to explore key variables and see which ones most affect the outcomes, even before the deep analysis by statisticians. This would help them make decisions about planning subsequent investigations. Data refinement Even though the data set has good quality, it is still necessary to do some additional modification to in order to present meaningful data for data exploration. Begin with data matrix, Days of Air Quality Equal to or Above Grade II (day) is the best variable scored 100% and the lowest
- 4. quality is Common Industrial Solid Wastes Disposed (56%). It is not a surprise because the data was collected cross the country that includes the heavy industrial cities and light industrial cities. The outliers are due to the nature of the data so it should be kept. For the same reason, variable Volume of Industrial sulfur dioxide (ton) should be treated the same even though it is median quality with score of 59%. Being said that data was collected from wide spread areas including heavy industry and light industry cities, a new variable was created grouping the cities into North, South, West and East and was used with the data exploration tool to examine the difference or similarity. A hierarchy “Pollution hierarchy” was created by using the “Annual Average Concentration of PM10”. And it was used in conjunction with the new variable “Total emission in waste gas waste” that was created by summarize the total industrial emission waste gas. Once again the northern cities indicated serious pollution level with PM10 value from 150 to 305 and followed by the west region. Conclusion By now, we can confidently conclude that the air pollution in China is serious. The overall mean PM10 indicates air is lightly polluted but that is misleading without considering the huge variances. The PM10 ranges from 47 – 305. The north region cities have the largest variances followed by west region cities. From the data exploration we can tell that the high population has an impact on living emissions. However, the emission in waste gas more significantly contributes to the air pollution. This is consistent with the nature of industry distribution in China. Since the data was collected during 2013 and 2014, it only tells the old story. The red alert of pollution issued in end of 2015 is definitely an encouragement that implies that Chinese government is taking it seriously.
- 5. References Ambient Air Quality in Key Cities of Environmental Protection (2013) http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ Hunt, K., Lu, S. (2015). Smog in China closes schools and construction sites, cuts traffic in Beijing; CNN. Retrieved From: http://www.cnn.com/2015/12/07/asia/china-beijing-pollution- red-alert/ Main Pullutant Emission in Waste Gas in Main Cities (2013). Data Retrieved from: http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ Main Pullutant Emission in Waste Gas in Main Cities (2014). Data Retrieved from: http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ Most populated cities in China. Data Retrieved from: http://www.nationsonline.org/oneworld/china_cities.htm Rohde, R., Muller, R. (2015) Air Pollution in China: Mapping of Concentrations and Sources. PLoS ONE 10(8): e0135749. doi: 10.1371/journal.pone.0135749; Retrieved From: http://journals.plos.org/plosone/article?id=10.1371/journal.pone .0135749 Wong, E. (2015). World Briefing | Asia; China: ‘Red Alert’ on Beijing’s Air. New York. P10. Retrieved From: http://www.nytimes.com/2015/12/18/world/asia/beijing-issues- a-second-red-alert-on-pollution.html Zhang, Y., Li, M., Bravo, M. A., Jin, L., Nori-Sarma, A., Xu, Y., … Bell, M. L. (2014). Air Quality in Lanzhou, a Major Industrial City in China: Characteristics of Air Pollution and Review of Existing Evidence from Air Pollution and Health Studies. Water, Air, and Soil Pollution, 225(10), 2187. http://doi.org/10.1007/s11270-014-2187-3 Appendix: Table 1.
- 6. City Year Annual Average Concentration of SO2 (μg/m3) Annual Average Concentration of NO2 (μg/m3) Annual Average Concentration of PM10 (μg/m3) 95th Percentile Daily Average Concentration of CO (mg/m3) 90th Percentile Daily Maximum 8 hours Average Concentration of O3 (μg/m3) Annual Average Concentration of PM25 (μg/m3) Days of Air Quality Equal to or Above Grade II (day) Beijing 2013 26 56 108 3.4 188 89 167 Changchun 2013 44 44 130 2.1 127 73 230 Changsha 2013 33 46 94 2.3 134 83
- 8. 259 Guiyang 2013 31 33 85 1.3 101 53 278 Haikou 2013 7 17 47 1.0 106 27 342 Hangzhou 2013 28 53 106 1.9 155 70 212 Table 2. City Year Volumn of Industrial sulfur dioxide (ton) Volumn of Industrial nitrogen oxides Emission (ton) Volumn of Industrial smoke ( powder ) dust (ton) Volumn of sulfur dioxide Emission by Consumption (ton)
- 9. Volumn of nitrogen oxides Emission by Consumption (ton) Volume of Consumption Soot Emission (ton) Beijing 2013 52041 75927 27182 34967 13638 28258 Changchun 2013 57246 95190 72970 7344 1545 7919 Changsha 2013 21173 15951 19545 2366 153 2946 Chengdu 2013 52040 44411 21452 4891 2109 661 Chongqing 2013
- 11. 17 5 Hangzhou 2013 82021 67283 40243 633 335 135 Table 3. City Year Industrial Waste Water Discharged (10,000 tons) Industrial COD Emission (ton) Industrial Ammonia Nitrogen (ton) Urban Living Waste Water Discharged (10,000 tons) Living COD Emission (ton) Living Ammonia Nitrogen (ton) Beijing 2013 9486 6055 330 134991 89868 14189 Changchun 2013 5482 11670 1384 20797 32006 6970
- 14. Exploration Figure 3. Exploration Figure 4. Exploration Figure 5. 1 Decision Management Systems url: https://watson.analytics.ibmcloud.com/product ibmid: [email protected] password: 779Qldqgwatson Assignment №2 – Exploratory Data Analysis (EDA) using Watson Analytics Deadline: Last day of week 5, 11:59 pm Eastern Time Submission via LEO. This is an individual assignment. Each student will complete the assignment outlined below and post his/her written results to the appropriate assignment. Please note that only 1 document is allowed to be submitted. See content on p.2. Grading criteria Submitted assignments will be graded for (a) content, (b) document quality (i.e. formatting, following guidelines, pleasant to read, etc.), and timeliness of submission. Assignments submitted late will be deducted 5 points for each
- 15. day it is late. Activities 1. Select from the dataset provided (or ones designated by your instructor). Provide a brief description of the datasets to include the number of cases, description of the inputs, description of the variables that could be used to develop predictive models, etc. 2. Examine the dataset and eliminate mistakes, bad records, data entry errors, and outliers. Using Watson Analytics: 3. Explore the dataset, including: a) Examine the initial set of questions posed by Watson Analytics. Provide any insights gained from this initial dataset. b) Develop new specific questions which provide additional insights into and answer specific questions from the dataset. Discuss how these insights could be useful. Did Watson Analytics provide the answers necessary? Discuss how you would improve the relevancy. c) Experiment with the available filters and visualization options at the bottom of the screen and summarize the results. Create and explain at least one insightful global and one local filter for your dataset. d) Create and explain at least one insightful calculation. Discuss why this would be useful. 4. Refine the dataset. a) Which variables have the highest quality score? Which ones have the lowest quality score and why? Discuss how the quality of the dataset could be improved. b) Utilize the available grouping, filtering and hierarchical functionalities to refine the data. Summarize the approach you
- 16. took and the outcome. What suggestions or insights are gained? Submission Each student will submit a single document conforming to the guidelines and standards outlined below. Document format: · limited to 5 pages (excludingtitle page, references, and appendix), · Double-spaced, 12 point Times New Roman font, 1” margins, Bottom-right page numbering. Note: Submitted report must be either in MS Word or PDF format and titled: “Assignment4_LastName”. Only one document will be allowed to submit. Content(note that the document must have clearly marked sections for the items listed below) 1) Title page (1 page limit): course number and term, assignment number and project title, student name and contact information, instructor’s name. Format it so it looks pleasant and presentable. Follow formatting guidelines above. 2) Introduction. Provide a brief outline of the dataset you are using for this assignment. Briefly explain the content of the data. Include a screenshot of the data (not all, but partial as far as all relevant variables are visible). 3) Discuss the data exploration process followed and the results. Include any specific ideas or suggestions as to how this could be used in your organization.
- 17. 4) Discuss the data refinement process followed and the results. 5) References (1 page limit): List all references in APA format used in preparing this report. It is strongly recommended to use outside knowledge in setting-up the analysis or discussing the results where possible. 6) Appendix (4 page limit): a) Appendix A: Include any appropriate workbooks and/or screenshots (figures, tables, diagrams) used in this assignment. Make sure all tables, figures, or diagrams are properly numbered and titled. For example, “Table 1. Model Results”. Make sure all tables or figures or diagrams are easily readable and visually presentable. General guidance · Assignments that: 1) adequately address all required tasks; 2) are submitted on time; 3) are properly formatted (APA format for references, no typos or misspelled words, no grammar errors, cover page, etc.) will receive a grade of B (80-89, depending on content). · In order to increase (but not guarantee) your chances of receiving a higher grade, you need to show clear evidence of critical thinking. Critical thinking can take many forms, depending on the type of assignment. In some instances, showing greater depth (e.g., such as creating more models, looking at more than one insightful fact or relationships, and comparing them on key criteria) is one method for providing evidence of critical thinking. In other cases, it might include providing more explanation to include the pros and cons of the approach used or the arguments in favor and against the proposal as well as some criteria for choosing among the alternatives. Still another example would be providing
- 18. significant insights as to how the assignment outcome would benefit (or would meet resistance) in your organization and what steps might be employed to facilitate acceptance. Certainly, this is not a complete list, but gives some examples of critical thinking aspects. Decision Management Systems Student Name: Assignment №2 – EDA using Watson Analytics Total points: 100 Content Explanation Points Comment Total Earned Written Report a. Introduction Is the dataset fully described and outlined? Is the intent of the assignment discussed at an appropriate level of detail? 10 b. Dataset cleansing
- 19. Is the dataset fully described and cleansed of outliers (as appropriate), mistakes and erroneous entries? 5 c. Data exploration a) Are the initial questions discussed and their relevance analyzed? Are insights from the questions provided? 15 b) Are additional specific questions developed and discussed? Are the answers provided by Watson Analytics insightful and do they provide specific answers? 20 c) Are the available filters and visualization options utilized and explained? Are insights provided from using the filters and visualization options? 15 a. Data refinement a) Are quality scores compared and explained? Are suggestions provided to improve the scores? 15
- 20. b) Are the grouping, filtering, and hierarchical features fully explored and summarized? Are suggestions for improving the outcomes provided? 15 b. Mechanics (spelling, grammar) Is the paper free of grammatical errors and spelling and punctuation? Is the paper properly formatted? 3 c. Citations and References Are all references and citations correctly written and presented? 2 Total 100 d) Less
- 21. 1. Formatting Does the submission follow formatting guidelines? - 5 2. Page limit Is the submission written within specified page limits? - 5 3. Late submission (less) 5 points will be deducted for each day the assignment is late. -5 each day Final Grade for Assignment 2