# Use of Social Media Data in Disaster Management: A Survey
## 6 Information Dimension
### Spatial
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{3}{c}{**Spatial**} & \multirow{2}{*}{**Pros and Cons**} \\ \cline{2-2} \cline{4-4}  & **Geo-Tagging** & & & & \\ \hline \hline \multirow{6}{*}{[34]} & \multirow{6}{*}{-} & Twitter users’ & Twitter users’ & The proposed approach can improve the accuracy of identifying the locations of hazard events. \\  & & city-level location estimation & & city-level location & The temporal aspect of the identified location needs to be further investigated for more accurate decision-making. \\ \hline \hline \multirow{6}{*}{[35]} & \multirow{6}{*}{-} & \multirow{6}{*}{profiling users’} & \multirow{6}{*}{profiling users’} & The approach can be applied to disseminate disaster early warning messages to social media users whose home locations are at risk \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \hline \hline \multirow{6}{*}{[36]} & \multirow{6}{*}{spatial pattern analysis} & \multirow{6}{*}{spatial pattern analysis} & \multirow{6}{*}{-} & This approach combines social media and sensor data for statistical analysis, which provides a more precise assessment of tweets’ spatial patterns in a hazardous area. \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \hline \hline \multirow{6}{*}{[37]} & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & This approach combines social media and sensor data for statistical analysis, which provides a more precise assessment of tweets’ spatial patterns in a hazardous area. \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \hline \hline \multirow{6}{*}{[38]} & \multirow{6}{*}{\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & The proposed method for spatial analysis enables timely decision-making for emergency response and full awareness of public concern. \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \cline{1-1} \cline{3-4}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Spatial. analysing the geographic distribution of words in tweets. The authors of [35] mentioned that geo-location information in tweet data may have noisy signals. For example, a user in the UK could tweet about a Houston Rockets game or their vacation in India. To overcome this, the authors integrate two types of signals (user's friend and user's tweet's nearby location) from social networks to predict a user's location. Geo-location analytics: The authors of [36] discussed the effect of an earthquake on the East Coast of the United States (US) on August 23, 2011 by analysing the collected tweet data. The main finding of the paper was the patterns between the distance from the epicentre and the time after the earthquake. The authors of [37] used sensor data to identify flood-affected regions. The authors performed some statistical analyses of the collected data to find the general spatial patterns and to explore the differences between the spatial patterns among the relevant tweets. On the other hand, methods such as kernel density estimation (KDE) have been widely used for clustering of the activities during Hurricane Sandy [38] and spatial hotspot detection during the 2012 Beijing rainstorm [3].