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

Benjamin G. Jacob, Robert J. Novak

Department of Global Health, University of South Florida, Tampa, USA.

DOI: 10.4236/ars.2014.33012                

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.

Keywords

Trimble Recon X, Vector Control Management System (VCMS), Anopheles arabiensis, QuickBird Imagery

Conflicts of Interest

The authors declare no conflicts of interest.

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