update to paper

joss/paper.bib

@@ -219,3 +219,36 @@ @Article{Peacock_2010
   groups    = {SVI},
   publisher = {Texas A&M University},
 }
+
+@article{VanZandt_2012,
+  title={Mapping social vulnerability to enhance housing and neighborhood resilience},
+  author={Van Zandt, Shannon and Peacock, Walter Gillis and Henry, Dustin W and Grover, Himanshu and Highfield, Wesley E and Brody, Samuel D},
+  journal={Housing Policy Debate},
+  volume={22},
+  number={1},
+  pages={29--55},
+  year={2012},
+  publisher={Taylor \& Francis}
+}
+
+@article{Xu_2024,
+  title={findSVI: an R package to calculate the Social Vulnerability Index at multiple geographical levels},
+  author={Xu, Heli and Li, Ran and Bilal, Usama},
+  journal={Journal of open source software},
+  volume={9},
+  number={99},
+  pages={6525},
+  year={2024}
+}
+
+
+@article{Cutter_2008,
+  title={Temporal and spatial changes in social vulnerability to natural hazards},
+  author={Cutter, Susan L and Finch, Christina},
+  journal={Proceedings of the national academy of sciences},
+  volume={105},
+  number={7},
+  pages={2301--2306},
+  year={2008},
+  publisher={National Acad Sciences}
+}

joss/paper.md

@@ -43,7 +43,11 @@ The exposure of communities to environmental hazards, their sensitivity to such
 
 
 # Statement of need
-Generic social vulnerability indices for large regions can be lacking in their ability to identify at risk populations [@Tate_2012; @Nelson_2015; @Tellman_2020]. Furthermore, not all vulnerability indices are created equal, and users, researchers, and developers should clearly state the objectives and structure of their index in order to accurately present their findings [@Bakkensen_2016]. *SVInsight* is an accessible and open-source tool to quickly calculate SVIs for a user-defined region using either custom or a research supported pre-set list of social and demographic variables. *SVInsight* calculates SVIs built on data from the Census Bureau's 5-Year American Community Survey using the two leading methods for calculating such metrics: a composite score using a data reduction methodology [@Cutter_2003] and a ranking method [@Flanagan_2011].
+Generic social vulnerability indices for large regions can be lacking in their ability to identify at risk populations [@Tate_2012; @Nelson_2015; @Tellman_2020]. Furthermore, not all vulnerability indices are created equal, and users, researchers, and developers should clearly state the objectives and structure of their index in order to accurately present their findings [@Bakkensen_2016]. Research utilizing SVIs has historically been limited to either existing national or regional databases [@Cutter_2003; @Flanagan_2011; VanZandt_2012; Bixler_2021a]. Researchers that want to incorporate social vulnerability information into their research are therefore limited to these existing databases and their pre-determined variable choices, or developing their own index from scratch. Furthermore, researchers’ understanding of what contributes to vulnerability is variable in both time and space [@Cutter_2008]. Therefore, it is necessary to tailor SVIs to the context in which they are being applied. 
+
+*SVInsight* is an accessible and open-source tool to quickly calculate SVIs for a user-defined region using either custom or a research supported pre-set list of social and demographic variables. This package creates a pipeline between extensively large datasets and easily customizable SVIs, allowing researchers to experiment and manipulate various indices (e.g., general vulnerability, economic, race/ethnicity, etc.) built on a variety of variables more efficiently and effectively.*SVInsight* calculates SVIs built on data from the Census Bureau's 5-Year American Community Survey using the two leading methods for calculating such metrics: a composite score using a data reduction methodology [@Cutter_2003] and a ranking method [@Flanagan_2011]. There is a recently published package in R that is capable of calculating the Rank Method SVI [@Xu_2024], but to the best of the author’s knowledge there is no open source factor analysis based SVI package.
+
+
 
 
 # Background