What are some common sources of error and bias in spatial data collection and analysis?

Once for one of the case studies regarding gummosis in Mangosteen in a home garden system, I needed to investigate about a possible soil fertility issue with that soil. and me and my colleague needed to get soil samples from where the problem was shown. Although the land area was large and there were clusters of trees each having the problem. thus we decided to get samples near to each tree cluster. there were about 5 tree clusters and because of the terrain and the availability of equipment we could only get one sample for each cluster, which may have caused bias . also another issue was the Mangosteen plants were of different age classes, so the heterogeneity of the population was also an issue.

In spatial analysis, sampling error is a critical consideration. It arises when data collected from a sample fails to accurately represent the entire population or area of interest. Factors like inadequate sample size, non-random sampling methods, and spatial autocorrelation contribute to this error. Its presence can distort spatial patterns, trends, and relationships, impacting the reliability of predictions and uncertainty assessments.

When getting soil samples from the field, it is good if we can measure the required properties in situ. usage of indirect methods such as sensors may come in handy for such measurements. although the predictive models used for those sensors to develop calibration curves need to be accurate. for example moisture content is a highly dynamic property, thus if we are going to measure moisture content in a laboratory it won't create field conditions most of the time. for that we need to minimize the exposure of the soil to atmosphere and minimize the effect of any external factor until bringing the sample to the laboratory to analyze.

Measurement error is a significant consideration in spatial analysis. It arises when observed spatial data deviate from the true values or states of the variables. Factors like human or instrument errors, calibration inaccuracies, and environmental noise contribute to this error. It introduces noise, bias, or distortion to spatial data, impacting the accuracy and reliability of predictions and uncertainty assessments.

Spatial misalignment is a critical issue in spatial analysis, arising when data don't align in units, scales, or projections. This discrepancy often stems from diverse data sources, resolutions, or coordinate systems. It leads to inconsistencies and mismatches among spatial data, impacting the reliability of predictions and uncertainty assessments. Resolving spatial misalignment is essential for ensuring the accuracy and robustness of spatial analysis results.

Spatial dependence, a common phenomenon in spatial analysis, refers to the correlation or similarity among neighboring locations or units. This correlation arises from spatial processes or interactions, unveiling spatial patterns and clusters. However, it can challenge statistical assumptions requiring spatial independence, impacting the validity of predictions and uncertainty assessments. Understanding and accounting for spatial dependence are crucial for accurate spatial analysis and informed decision-making.

Spatial heterogeneity, a prevalent aspect of spatial analysis, denotes the diversity or variability across different locations or units in spatial data. It arises from factors like spatial variation or complexity, reflecting spatial differences and gradients. While it enriches understanding of spatial patterns, it can challenge assumptions of methods requiring spatial homogeneity, impacting the reliability of predictions and uncertainty assessments. Acknowledging and addressing spatial heterogeneity is vital for accurate spatial analysis and robust decision-making.