This visualization represents the total cases and total deaths according to the year and state selection. Here we have five different diseases. These are Acute Diahorreal, Acute Respiratory, Japanese Encephalitis, Malaria and Viral Hepatitis. We have total cases and deaths out of those cases for those diseases. Select the state and years from years filter and check the cases for the disease deaths for that disease. This visualization tells us about the state wise disease case and deaths for a particular year. We can analyze the data by comparing the increase/decrease in cases and deaths by state/disease and by year. Below is a scree-shot for the registered cases/deaths for the above mentioned five diseaes for the state of Andhra Pradesh in the years 2010 and 2011.As observed, a huge number of registered cases and deaths are due to acute diarrhoeal and respiratory diseases. And hence relevant measures can be taken to reduce the severity of these diseases. Though the number of cases and deaths for the other diseases is not as high as the above mentioned two, they are significant and appropriate measure can be taken to reduce the cases and deaths.
This visualization shows All India data pertaining to five diseases Diarrhea, Respiratory Infection, Japanese Encephalitis, Malaria and Viral Hepatitis. It also has data related to health expenditure incurred by the Indian government. It provides trends for the above mentioned data and these trends can be analyzed to study the expenditure incurred by the Indian government for the years from 2002 till 2010 on health related services to reduce the severity of several diseases.The above visualization shows an increase in the health expenditure and also decreasing trends for both the cases and deaths for acute respiratory infection. Though the decrease in the number of registered cases in not high, further analysis can be done so as to determine the underlying events that are responsible for the resulting trend. Similar analysis can be done for the other diseases also.
This is an interactive visualization which shows All India data pertaining to five diseases Diarrhea, Respiratory Infection, Japanese Encephalitis, Malaria and Viral Hepatitis. It also has data pertaining to Water Pollution Parameters like Biochemical Oxygen Demand, Conductivity, Dissolved Oxygen, Fecal Coliform, pH, Temperature, Total Coliform. It provides trends for the above mentioned data and these trends can be analyzed to study how each water pollution parameter has effect on cases and deaths for a particular disease.In the above visualization it can be observed that the total coliform in the river Satluj shows an increasing trend over the years. And a corresponding increase in trend can be noticed for the total registered cases and deaths for Japanese Encephalitis. Similar study can be done for other parameters for different rivers and diseases. The adverse effects of the mentioned water pollution parameters and its relation to cases and deaths for diseases can be thus analyzed.
This visualization combines four datasets that have data related to several villages across India where the quality of water had been affected. “Arsenic”, “Fluoride”, “Iron”, “Nitrate” and “Salinity” are the water quality parameters that have been used. The data is further segregated and filtered by year. Below is a screen-shot of the visualization. As seen in the above visualization, there are different plots in the visualization. This visualization is interactive and can be used to filter data. The applied filters are state, district, year and water quality parameters. On the top left corner is the state plot where a specific state can be selected to filter the data for that particular state. In the top right corner is the district plot which has all the districts shown initially. But once the state plot is filtered for a particular state, the district plot shows districts of the selected state. The district plot can again be used as a filter to filter the data in the data plot i.e. the table at the bottom, which then displays affected villages for that particular district. Apart from the plot filters, we have the filters for year and water quality parameter. The year filter is used to display data related to a particular year. The water quality parameters are color coded and this code can also be seen in the card below the water quality parameter filter. The plots are further explained below.
State plot – The state plot highlights the states with color if data pertaining to that particular state is available in the datasets. The state plot also has color gradient which represents the number of affected villages. Darker the color gradient, higher is the number of affected villages. Hovering the mouse over the state displays a tooltip with the name of the state and also the number of affected villages in that particular state. The state plot can also be used as a filter to select a particular state. The filter can be removed by clicking elsewhere on the plot or using the Esc key and on the keyboard or by simply refreshing the page.
District plot – The district plot is a packed bubbles chart, which shows all the districts filtered by the state plot. Each circle represent a district. The size of the circle represents the number of affected villages and the color the water quality parameter. Hovering the mouse over the circle shows a tooltip with the name of the district and also the number of affected villages in that particular district. The district plot can also be used as a filter to select a particular district. The filter can be removed by clicking elsewhere on the plot or using the Esc key on the keyboard or by simply refreshing the page.
Data plot/table – The data plot or the table is used to display a list of affected villages based on the above discussed applied parameters/filters. As can be seen the village is segregated in a hierarchy by in order from left by district, block, panchayat and habitation. Hovering the mouse over the table displays ‘+’ symbols which can be click to collapse a level in the data plot.
This visualization as explained combines four different datasets to present data related to different villages affected by above mentioned water quality parameters. This powerful, interactive visualization presents data to the user in a more simplistic/uncomplicated form.
This interactive visualization displays water quality data for the three years 2008,2011 and 2012. The map plot on the left shows the statewise distribution of the average of the selected water quality parameter, aggregated by state. A total of twenty eight water quality parameters are available in the drop down list for the user to filter through. The packed bubbles plot on the right shows the countrie’s aggregated average value of the selected water quality for the parameter for the selected year. Hovering the mouse over the visualization displays information related to a specific element like a state or a bubble.
These interactive visualization’s can be put together with the disease trends visualization to arrive at conclusions related to the influence of different parameters on the number of cases/deaths for different diseases.
This visualization is built combining eighty one (81) different datasets, all of which have data related to water quality. Three year’s (i.e. 2008, 2011 and 2012) data has been combined from the above mentioned datasets. The number of datasets for each year is listed below-
2008 – 38 datasets
2011 – 1 dataset
2012 – 42 datasets
The above listed datasets have been downloaded from the links below-
2008 & 2011 - https://data.gov.in/catalog/status-water-quality-india-2008-and-2011
2012 - https://data.gov.in/catalog/status-water-quality-india-2012
This interactive visualization displays cases and deaths data for the two years 2010 and 2011. The map plot on the top shows the statewise distribution of the cases/deaths for the selected disease recorded in the state filtered by the year. Below is the bar plot which shows the cases/deaths for the selected disease compared for 2 years. Bar plot is filtered using the map plot, by simply selecting a specific state.