Share This Article:

Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU® Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control

Abstract Full-Text HTML Download Download as PDF (Size:4622KB) PP. 141-196
DOI: 10.4236/ars.2014.33012    3,984 Downloads   4,748 Views   Citations

ABSTRACT

In this research, we determined the feasibility of using a Personal Digital Assistant (PDA) as a mobile field data collection system by monitoring mapping and regressing digitized sub-meter resolution polygons of multiple, malaria, mosquito, Anopheline arabiensis s.s., aquatic, larval, habitat covariates. The system employed QuickBird raster imagery displayed on a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU®. The mobile mapping platform was employed to identify specific geographical locations of treated and untreated seasonal An. arabiensis s.s. aquatic larval habitats in Karima rice-village complex in the Mwea Rice Scheme, Kenya. As data pertaining to An. arabiensis s.s. larval habitats were entered, all treated and untreated rice paddies within a 2 km buffer of the agro-village, riceland-complex, epidemiological, study site were viewed and managed on the PDA.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Jacob, B. and Novak, R. (2014) Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU® Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control. Advances in Remote Sensing, 3, 141-196. doi: 10.4236/ars.2014.33012.

References

[1] Sattler, M.A., Mtasiwa, D., Kiama, M., Premji, Z., Tanner, M., Killeen, G.F. and Lengeler, C. (2005) Habitat Characterization and Spatial Distribution of Anopheles sp Mosquito Larvae in Dar es Salaam (Tanzania) during an Extended Dry Period. Malaria Journal, 4, 4-11. http://dx.doi.org/10.1186/1475-2875-4-4
[2] Jacob, B.G., Muturi, E.J., Funes, J., Shililu, J., Githure, J.I., Regens, J.L. and Novak, R.J. (2007) Using Imaging Technologies to Control Malaria. Imaging Notes, 22, 17-22.
[3] Gu, W. and Novak, R.J. (2005) Habitat-Based Modeling of Impacts of Mosquito Larval Interventions on Entomological Inoculation Rates, Incidence, and Prevalence of Malaria. American Journal of Tropical Medicine and Hygiene, 11, 546-552.
[4] CDC (Centers for Disease Control and Prevention). The President’s Malaria Initiative (IPM) Rolls into Action. http://www.cdc.gov.
[5] Jensen, J.R. (2005) Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd Edition, Prentice-Hall, Upper Saddle River.
[6] Environmental Systems Research Institute. http://www.esri.com
[7] Digital Globe LTD. http://digitalglobe.com
[8] Muturi, E.J., Mwangangi, J., Shililu, J., Muriu, S.M., Jacob, B.G., Kabiru, E.W., Gu, W., Mbogo, C.M., Githure, J. and Novak, R.J. (2007) Mosquito Species Succession and Physicochemical Factors Affecting Their Abundance in Rice Fields in Mwea, Kenya. Journal of Medical Entomology, 44, 336-344. http://dx.doi.org/10.1603/0022-2585(2007)44[336:MSSAPF]2.0.CO;2
[9] Jacob, B.G., Nelson, P., Lampman, R., Morris, J., Raim, A., Funes, J., LaPointe, C. and Novak, R.J. (2006) Comparing GPS Technology for Identifying Spatial Ecological Variation for Urban Mosquito Management. Wing Beats, 16, 30-33.
[10] Gillies, M.T. and Coetzee, M. (1987) A Supplement to the Anophelinae of Africa South of the Sahara. South African Institute for Medical Research, 55, 1-143.
[11] Service, M.W. (1993) Mosquito Ecology: Field Sampling Methods. 2nd Edition, Elsevier Publishers, Essex.
[12] Edwards, F.W. (1941) Notes on British Mycetophilidae.
[13] Bioequip Products Inc. www.bioquip.com
[14] Meritt, R.W. and Cummins, K.W. (1996) An Introduction to the Aquatic Insects of North America. 3rd Edition, Kendall Hunt Editor, Dubuque.
[15] ERDAS. http://geospatial.intergraph.com/Homepage.aspx
[16] Rejmankova, E., Roberts, D., Pawley, A., Manguin, A. and Polanco, S. (1995) Predictions of Adult Anopheles albimanus Densities in Villages Based on Distances to Remotely Sensed Larval Habitats. American Journal of Tropical Medicine and Hygiene, 53, 482-491.
[17] Beck, L.R., Rodriguez, M.H., Dister, S.W., Rodriguez, A.D., Rejmankova, E., Ulloa, A., Meza, R.A., Roberts, D.R., Paris, J.F. and Spanner, M.A. (1994) Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High risk Malaria Transmission. American Journal of Tropical Medicine and Hygiene, 51, 271-280.
[18] Wood, B.R., Washino, R., Beck, L., Hibbard, K., Pitcairn, M., Roberts, D., Rejmankova, E., Paris, J., Hacker, C., Salute, J., Sebesta, P. and Legters, L. (1991) Distinguishing High and Low Anopheline-Producing Rice Fields Using Remote Sensing and GIS Technologies. Preventive Veterinary Medicine, 11, 277-288. http://dx.doi.org/10.1016/S0167-5877(05)80014-5
[19] Clarke Mosquito Control Products, Inc., Roselle, IL for the VCMS, Mobile VCMS and FieldBridge Software. https://www.clarkemosquito.com/vcms_tech_support
[20] Jacob, B.G., Muturi, E.J., Funes, J.E., Shililu, J.I., Githure, J.I., Kakoma, I.I. and Novak, R.J. (2006) A Grid-Based Infrastructure for Ecological Forecasting of Rice Land Anopheles arabiensis Aquatic Larval Habitats. Malaria Journal, 5, 941-947. http://dx.doi.org/10.1186/1475-2875-5-91
[21] Dolo, G., Bri?t, O.J., Dao, A., Traoré, S.F., Bouaré, M., Sogoba, N. and Touré, Y.T. (2004). Malaria Transmission in Relation to Rice Cultivation in the Irrigated Sahel of Mali. Acta Tropica, 89, 147-159. http://dx.doi.org/10.1016/j.actatropica.2003.10.014
[22] Green, K., Kempka, D. and Lackley, L. (1994) Using Remote Sensing to Detect and Monitor Land Cover and Land Use Changes. Photogrammetric Engineering and Remote Sensing, 60, 331-337.
[23] Congalton, R.G. (1991) A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, 35-46. http://dx.doi.org/10.1016/0034-4257(91)90048-B
[24] Arbia, G. (1988) Spatial Data Configuration in the Statistical Analysis of Regional Economics and Related Problems. Kluwer, Dordrecht.
[25] Jacob, B.G., Griffith, D., Gunter, J., Muturi, E.J., Caamano, E., Shililu, J., Guthure, J., Regens, J. and Novak, R.J. (2009) A Spatial Filtering Specification for an Auto-Negative Binomial Model of Anopheles arabiensis Aquatic Habitats. Transactions in GIS, 12, 515-539. http://dx.doi.org/10.1111/j.1467-9671.2008.01110.x

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.