Landscape Ecology

, Volume 34, Issue 6, pp 1295–1311 | Cite as

An operational machine learning approach to predict mosquito abundance based on socioeconomic and landscape patterns

  • Shi ChenEmail author
  • Ari Whiteman
  • Ang Li
  • Tyler Rapp
  • Eric Delmelle
  • Gang Chen
  • Cheryl L. Brown
  • Patrick Robinson
  • Maren J. Coffman
  • Daniel Janies
  • Michael Dulin
Research Article



Socioeconomic and landscape factors influence mosquito abundance especially in urban areas. Few studies addressed how socioeconomic and landscape factors, especially at micro-scale for mosquito life history, determine mosquito abundance.


We aim to predict mosquito abundance based on socioeconomic and/or landscape factors using machine learning framework. Additionally, we determine these factors’ response to mosquito abundance.


We identified 3985 adult mosquitoes (majority of which were Aedes mosquitoes) in 90 sampling sites from Charlotte, NC, USA in 2017. Seven socioeconomic and seven landscape factors were used to predict mosquito abundance. Three supervised learning models, k-nearest neighbor (kNN), artificial neural network (ANN), and support vector machine (SVM) were constructed, tuned, and evaluated using both continuous input factors and binary inputs. Random forest (RF) was used to assess individual input’s relative importance and response to mosquito abundance.


We showed that landscape factors alone yielded equal or better predictability than socioeconomic factors. The inclusion of both types of factors further improved model accuracy using binary inputs. kNN also had robust performance regardless of inputs (accuracy > 95% for binary and > 99% for continuous input data). Landscape factors group had higher importance than socioeconomic group (54.4% vs. 45.6%). Landscape heterogeneity (measured by Shannon index) was the single most important input factor for mosquito abundance.


Landscape factors were the key for mosquito abundance. Machine learning models were powerful tools to handle complex datasets with multiple socioeconomic and landscape factors to accurately predict mosquito abundance.


Socioeconomic gradient Landscape heterogeneity Mosquito abundance Machine learning Urban ecology 



Vector-borne diseases


Population size factor


Income factor


Employment rate factor


Education status factor


Population density factor


Home sale price factor


Violent crime rate factor


Tree canopy factor


Grass factor


Building factor


Road factor


Shannon index


Simpson index


k-Nearest neighbor


Artificial neural network


Support vector machine


Generalized linear model


Random forest


Non-metric multidimensional scaling


True positive


True negative


False positive


False negative


Root mean squared error



We thank Mecklenburg County Health Department for providing funding for the mosquito sampling work in 2017 and access to the orthophotos. We are also grateful for the field work of ten volunteering undergraduate students from UNC Charlotte.

Supplementary material

10980_2019_839_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 15 kb)


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Shi Chen
    • 1
    • 5
    • 6
    Email author
  • Ari Whiteman
    • 2
  • Ang Li
    • 3
  • Tyler Rapp
    • 2
  • Eric Delmelle
    • 2
  • Gang Chen
    • 2
  • Cheryl L. Brown
    • 4
  • Patrick Robinson
    • 6
  • Maren J. Coffman
    • 6
  • Daniel Janies
    • 7
  • Michael Dulin
    • 1
    • 2
    • 6
  1. 1.Department of Public Health SciencesUniversity of North Carolina CharlotteCharlotteUSA
  2. 2.Department of Geography and Earth SciencesUniversity of North Carolina CharlotteCharlotteUSA
  3. 3.State Key Laboratory of Vegetation and Environmental ChangeChinese Academy of SciencesBeijingChina
  4. 4.Department of Political Science and Public AdministrationUniversity of North Carolina CharlotteCharlotteUSA
  5. 5.Data Science InitiativeUniversity of North Carolina CharlotteCharlotteUSA
  6. 6.Academy for Population Health Innovation, College of Health and Human ServicesUniversity of North Carolina at CharlotteCharlotteUSA
  7. 7.Department of BioinformaticsUniversity of North Carolina CharlotteCharlotteUSA

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