Spatial statistical analysis
The Covid-19 pandemic situation was dire in New York City (NYC), prompting immediate measures to mitigate the transmission. These Stay-At-Home (SAH) measures altered the geographical distribution and rate of crimes. In this study, the Inhomogeneous Cross L-Function and global and local spatial au...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175637 |
| _version_ | 1811681011436617728 |
|---|---|
| author | Oreena Raveendran |
| author2 | Fedor Duzhin |
| author_facet | Fedor Duzhin Oreena Raveendran |
| author_sort | Oreena Raveendran |
| collection | NTU |
| description | The Covid-19 pandemic situation was dire in New York City (NYC), prompting immediate measures to
mitigate the transmission. These Stay-At-Home (SAH) measures altered the geographical distribution
and rate of crimes. In this study, the Inhomogeneous Cross L-Function and global and local spatial
autocorrelation methods – Moran’s I test, Geary’s C test, Local Moran, Local Geary and Getis-Ord – were used to analyse this change. From the Inhomogeneous Cross L-Function, we found that
there is spatial randomness between 0 − 50m, clustering between 50m − 170m and inhibition beyond
170m for the two spatial processes: Covid-19 and Crime (7 types). Furthermore, using global spatial
autocorrelation methods, we deduced that Covid-19 did not affect the overall spatial distribution of
crimes. Lastly, the local spatial autocorrelation methods allowed us to understand the change in spatial
patterns across NYC for the 7 types of crimes from the pre-pandemic period, to during pandemic and
also the post-pandemic period. We also analysed the variation in formulas and results from these local
spatial autocorrelation methods. Overall, the results drawn from this paper suggest that Covid-19 did
not have as significant of an impact on crimes despite NYC being the hardest hit city in the US from
this virus. |
| first_indexed | 2024-10-01T03:34:10Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175637 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:34:10Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1756372024-05-06T15:36:45Z Spatial statistical analysis Oreena Raveendran Fedor Duzhin School of Physical and Mathematical Sciences FDuzhin@ntu.edu.sg Mathematical Sciences Spatial analysis The Covid-19 pandemic situation was dire in New York City (NYC), prompting immediate measures to mitigate the transmission. These Stay-At-Home (SAH) measures altered the geographical distribution and rate of crimes. In this study, the Inhomogeneous Cross L-Function and global and local spatial autocorrelation methods – Moran’s I test, Geary’s C test, Local Moran, Local Geary and Getis-Ord – were used to analyse this change. From the Inhomogeneous Cross L-Function, we found that there is spatial randomness between 0 − 50m, clustering between 50m − 170m and inhibition beyond 170m for the two spatial processes: Covid-19 and Crime (7 types). Furthermore, using global spatial autocorrelation methods, we deduced that Covid-19 did not affect the overall spatial distribution of crimes. Lastly, the local spatial autocorrelation methods allowed us to understand the change in spatial patterns across NYC for the 7 types of crimes from the pre-pandemic period, to during pandemic and also the post-pandemic period. We also analysed the variation in formulas and results from these local spatial autocorrelation methods. Overall, the results drawn from this paper suggest that Covid-19 did not have as significant of an impact on crimes despite NYC being the hardest hit city in the US from this virus. Bachelor's degree 2024-05-02T02:35:05Z 2024-05-02T02:35:05Z 2024 Final Year Project (FYP) Oreena Raveendran (2024). Spatial statistical analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175637 https://hdl.handle.net/10356/175637 en application/pdf Nanyang Technological University |
| spellingShingle | Mathematical Sciences Spatial analysis Oreena Raveendran Spatial statistical analysis |
| title | Spatial statistical analysis |
| title_full | Spatial statistical analysis |
| title_fullStr | Spatial statistical analysis |
| title_full_unstemmed | Spatial statistical analysis |
| title_short | Spatial statistical analysis |
| title_sort | spatial statistical analysis |
| topic | Mathematical Sciences Spatial analysis |
| url | https://hdl.handle.net/10356/175637 |
| work_keys_str_mv | AT oreenaraveendran spatialstatisticalanalysis |