Oliver Bieh-Zimmert
Detailed View on Cancer Statistics
The analysis of statistical data about cancer occurence is an essential task in epidemiology. It is considerable to identify risk factors in the early stage of occurrence. For this task the German Centre for Cancer Registry Data is collecting the cancer occurrence in Germany. The effort to collect cancer registry data is important to investigate shot-, mid- and long-term effects of cancer occurrence and the evaluation of health care policy measures targeting cancer prevention, early tumor detection, cancer treatment and care.
The challenge is that every cancer occurence must be analysed under different perspectives. Every occurence contains information about the age, gender, localization of the cancer (e.g. lung) and implication. Through this multidimensional view the visualization of cancer statistics must cover these properties simultaneously. The choice was to visualize the cancer occurence along these properties to allow the user a discovery process. If you have a massive data set it es necessary to recognize patterns or intersting relationships. The flow visualization helps you to identify cancer occurence of specific groups and to see the value distribution along the other properties.
The analysis of statistical data about cancer occurence is an essential task in epidemiology. It is considerable to identify risk factors in the early stage of occurrence. For this task the German Centre for Cancer Registry Data is collecting the cancer occurrence in Germany. The effort to collect cancer registry data is important to investigate shot-, mid- and long-term effects of cancer occurrence and the evaluation of health care policy measures targeting cancer prevention, early tumor detection, cancer treatment and care.
The challenge is that every cancer occurence must be analysed under different perspectives. Every occurence contains information about the age, gender, localization of the cancer (e.g. lung) and implication. Through this multidimensional view the visualization of cancer statistics must cover these properties simultaneously. The choice was to visualize the cancer occurence along these properties to allow the user a discovery process. If you have a massive data set it es necessary to recognize patterns or intersting relationships. The flow visualization helps you to identify cancer occurence of specific groups and to see the value distribution along the other properties.
Try out the visualization at www.visual-telling.com.
Detailed View on Cancer Statistics
The analysis of statistical data about cancer occurence is an essential task in epidemiology. It is considerable to identify risk factors in the early stage of occurrence. For this task the German Centre for Cancer Registry Data is collecting the cancer occurrence in Germany. The effort to collect cancer registry data is important to investigate shot-, mid- and long-term effects of cancer occurrence and the evaluation of health care policy measures targeting cancer prevention, early tumor detection, cancer treatment and care.
The challenge is that every cancer occurence must be analysed under different perspectives. Every occurence contains information about the age, gender, localization of the cancer (e.g. lung) and implication. Through this multidimensional view the visualization of cancer statistics must cover these properties simultaneously. The choice was to visualize the cancer occurence along these properties to allow the user a discovery process. If you have a massive data set it es necessary to recognize patterns or intersting relationships. The flow visualization helps you to identify cancer occurence of specific groups and to see the value distribution along the other properties.
The analysis of statistical data about cancer occurence is an essential task in epidemiology. It is considerable to identify risk factors in the early stage of occurrence. For this task the German Centre for Cancer Registry Data is collecting the cancer occurrence in Germany. The effort to collect cancer registry data is important to investigate shot-, mid- and long-term effects of cancer occurrence and the evaluation of health care policy measures targeting cancer prevention, early tumor detection, cancer treatment and care.
The challenge is that every cancer occurence must be analysed under different perspectives. Every occurence contains information about the age, gender, localization of the cancer (e.g. lung) and implication. Through this multidimensional view the visualization of cancer statistics must cover these properties simultaneously. The choice was to visualize the cancer occurence along these properties to allow the user a discovery process. If you have a massive data set it es necessary to recognize patterns or intersting relationships. The flow visualization helps you to identify cancer occurence of specific groups and to see the value distribution along the other properties.
Try out the visualization at www.visual-telling.com.