Sampling Spatial Units for Agricultural Surveys pp 219-237 | Cite as
Survey Data Collection and Processing
Chapter
First Online:
Abstract
Classic sampling theory textbooks do not devote a considerable amount of attention to the necessary activities between designing the sample and estimating the parameters of the variables of interest. This disconnect is mainly because the methods used for these activities are often poorly developed. However, these phases of the survey have a considerable impact on both the cost and quality of the results. This chapter presents the various activities that occur during data collection and processing and how they should be organized and conducted.
Keywords
Land Cover Land Cover Type Confusion Matrix Statistical Unit Commission Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
- Atkinson D, House CC (2010) A generalized edit and analysis system for agricultural data. In: Benedetti R, Bee M, Espa G, Piersimoni F (eds) Agricultural survey methods. Wiley, Chichester, pp 233–242Google Scholar
- Berthelot JM, Latouche M (1993) Improving the efficiency of data collection: a generic respondent follow-up strategy for economic surveys. J Bus Econ Stat 11:417–424Google Scholar
- Carfagna E, Marzialetti J (2009a) Sequential design in quality control and validation of land cover databases. Appl Stoch Model Bus Ind 25:195–205CrossRefGoogle Scholar
- Carfagna E, Marzialetti J (2009b) Continuous innovation of the quality control of remote sensing data for territory management. In: Erto P (ed) Statistics for innovation. Springer, Italia, pp 145–160CrossRefGoogle Scholar
- De Waal T (2009) Statistical data editing. In: Pfeffermann D, Rao CR (eds) Handbook of statistics 29A, sample surveys: design, methods and applications. Elsevier, The Netherlands, pp 187–214CrossRefGoogle Scholar
- De Waal T, Pannekoek J (2010) Statistical data editing for agricultural surveys. In: Benedetti R, Bee M, Espa G, Piersimoni F (eds) Agricultural survey methods. Wiley, Chichester, pp 243–265CrossRefGoogle Scholar
- De Waal T, Pannekoek J, Scholtus S (2011) Handbook of statistical data editing and imputation. Wiley, Hoboken, NJGoogle Scholar
- Eurostat (2009a) LUCAS 2009, technical reference document: C-4. Quality control procedures. EurostatGoogle Scholar
- Eurostat (2009b) LUCAS 2009, M2—Quality assurance. EurostatGoogle Scholar
- Eurostat (2013) LUCAS 2012, technical reference document: C-1 instructions for surveyors. EurostatGoogle Scholar
- Foody GM (2002) Status of land cover classification accuracy. Remote Sens Environ 80:185–201CrossRefGoogle Scholar
- Forsman G, Shreiner I (1991) The design and analysis of reinterview: an overview. In: Biemer PP, Groves RM, Lyberg LE, Mattiowetz NA, Sudman S (eds) Measurement error in surveys. Wiley, New York, NY, pp 279–301Google Scholar
- Fowler FJ (2009) Survey research methods, 4th edn. Sage Publications, Thousand Oaks, CAGoogle Scholar
- Latouche M, Berthelot JM (1992) Use of score function to prioritize and limit recontacts in editing business surveys. J Off Stat 8:389–400Google Scholar
- Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107:606–616CrossRefGoogle Scholar
- Lunetta RS, Congalton RG, Fenstemarker LK, Jensen JR, McGwire KC, Tinney LR (1991) Remote sensing and geographic information system data integration: error sources and research issues. Photogr Eng Remote Sens 57:677–687Google Scholar
- Madans J, Miller K, Maitland A, Willis G (2011) Question evaluation methods. Wiley, Hoboken, NJCrossRefGoogle Scholar
- Nusser SM, Klaas EE (2003) Survey methods for assessing land cover map accuracy. Environ Ecol Stat 10:309–331CrossRefGoogle Scholar
- Scepan J (1999) Thematic validation of high-resolution global land-cover data sets. Photogr Eng Remote Sens 65:1051–1060Google Scholar
- Statistics Canada (2009) Statistics Canada quality guidelines, 5th edn. Minister of Industry, OttawaGoogle Scholar
- Statistics Canada (2010) Survey methods and practices. Minister of Industry, OttawaGoogle Scholar
- Stehman SV (1995) Thematic map accuracy assessment from the perspective of finite population sampling. Int J Remote Sens 16:589–593CrossRefGoogle Scholar
- Stehman SV (2001) Statistical rigor and practical utility in thematic map accuracy assessment. Photogr Eng Remote Sens 67:727–734Google Scholar
- Story M, Congalton R (1986) Accuracy assessment: a user’s perspective. Photogr Eng Remote Sens 52:397–399Google Scholar
- Strahler AH, Boschetti L, Foody GM, Friedl MA, Hansen MC, Herold M, Mayaux P, Morisette JT, Stehman SV, Woodcock CE (2006). Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. GOFC-GOLT report no 25. Office for Official Publication of the European Communities, LuxemburgGoogle Scholar
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