| Type: | Package |
| Title: | A Versatile Kernel Density Visualization Library for Geospatial Analytics (Heatmap) |
| Version: | 1.1 |
| Maintainer: | Bojian Zhu <bjzhu999@gmail.com> |
| Description: | Unlock the power of large-scale geospatial analysis, quickly generate high-resolution kernel density visualizations, supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) <doi:10.1145/3555041.3589401>. Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) <doi:10.1145/3555041.3589711>. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.1145/3514221.3517823>. Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3554821.3554855>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3503585.3503591>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3494124.3494135>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) <doi:10.14778/3476311.3476312>. Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) <doi:10.14778/3461535.3461540>. Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) <doi:10.1145/3318464.3380561>. Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) <doi:10.1109/TKDE.2020.3018376>. Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) <doi:10.1109/ICDE.2019.00055>. |
| URL: | https://github.com/bojianzhu/Rlibkdv |
| BugReports: | https://github.com/bojianzhu/Rlibkdv/issues |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Imports: | leaflet, raster, magrittr, Rcpp, sf |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| LinkingTo: | Rcpp |
| NeedsCompilation: | yes |
| Packaged: | 2023-10-20 11:04:39 UTC; bojianzhu |
| Author: | Bojian Zhu [cre, aut], Tsz Nam Chan [aut], Leong Hou U [aut], Dingming Wu [aut], Jianliang Xu [aut], LibKDV Group [cph] |
| Repository: | CRAN |
| Date/Publication: | 2023-10-21 23:50:05 UTC |
Hong Kong COVID-19 Cases Dataset
Description
This dataset contains the COVID-19 cases data in Hong Kong.
Usage
hk
Format
A data frame with 3 variables:
- lon
Longitude of the location
- lat
Latitude of the location
- t
Number of COVID-19 cases
Use KDV
Description
Efficient and accurate kernel density visualization.
Usage
kdv(
longitude,
latitude,
bandwidth_s = 1000,
row_pixels = 800,
col_pixels = 640
)
Arguments
longitude |
features' longitude |
latitude |
features' latitude |
bandwidth_s |
spatial bandwidth |
row_pixels |
row pixels |
col_pixels |
col pixels |
Value
kdv result
Examples
data(hk)
resKDV <- kdv(hk$lon, hk$lat, 1000, 800 ,640)
kernel density visualization in C++
Description
kernel density visualization in C++
Usage
kdvCpp(args)
Arguments
args |
arguments for kdv |
Value
the kdv result
Plot KDV
Description
Plot KDV
Usage
plotKDV(data)
Arguments
data |
result of kdv |
Value
No return value, called to plot KDV heatmap
Examples
data(hk)
resKDV <- kdv(hk$lon, hk$lat, 1000, 800 ,640)
plotKDV(resKDV)
Plot STKDV
Description
Plot STKDV
Usage
plotSTKDV(data)
Arguments
data |
result of stkdv |
Value
No return value, called to plot STKDV heatmap
Examples
data(hk)
resSTKDV <- stkdv(hk$lon, hk$lat, hk$t, 1000, 6, 800, 640, 32)
plotSTKDV(resSTKDV)
Use STKDV
Description
Efficient and accurate spatiotemporal kernel density visualization.
Usage
stkdv(
longitude,
latitude,
time,
bandwidth_s = 1000,
bandwidth_t = 6,
row_pixels = 800,
col_pixels = 640,
t_pixels = 32
)
Arguments
longitude |
features' longitude |
latitude |
features' latitude |
time |
features' time |
bandwidth_s |
spatial bandwidth |
bandwidth_t |
temporal bandwidth |
row_pixels |
row pixels |
col_pixels |
col pixels |
t_pixels |
time pixels |
Value
stkdv result
Examples
data(hk)
resSTKDV <- stkdv(hk$lon, hk$lat, hk$t, 1000, 6, 800, 640, 32)