Template and Guidelines for Writing Geospatial Analysis Articles

Purpose of This Document

This document provides a general structure and writing guidance for research papers involving geospatial analysis. It is designed to promote consistency, clarity, and methodological transparency across disciplines such as environmental science, built environment, ecology, geography, and social sciences.

Each section outlines key elements and best practices, which can be tailored according to the study’s objectives and data characteristics.

Recommended Article Structure

1. Introduction

Purpose: Establish the research context, significance, and objectives.

Key Elements:

• Background: Importance of spatial perspective in the studied domain.
• Problem statement (review of existing studies in terms of topics and methods): Why spatial analysis is necessary (e.g., spatial heterogeneity, accessibility, spatial dependence). Here are some commonly used concepts in spatial analysis:
  • Spatial association: second-dimension spatial association, interactive detector for spatial association, local pathways of association
  • Spatial autocorrelation: heterogeneous spatial autocorrelation,
  • Geostatistics or Kriging: segment-based regression kriging,
  • Spatial heterogeneity: spatial stratified heterogeneity, local stratified heterogeneity, generalized heterogeneity, geographically optimal zones-based heterogeneity, locally explained heterogeneity, wavelet geographically weighted regression, spatio-temporal unmixing with heterogeneity,  
  • Spatial interaction: robust Interaction, interactive detector for spatial association, geographical pattern interaction,
  • Geographical similarity: geographically optimal similarity,
  • Geocomplexity: geocomplexity,
  • Spatial graph network: geographical graph neural network, dynamic spatiotemporal graph network,
  • Spatial fusion: spatial context-aware fusion,
  • Spatial anisotropy: spatial irregular anisotropy,
  • Spatial accessibility: D2SFCA spatiotemporal accessibility,
  • Spatial decision-making: MFSD spatial decision making,
  • Spatial big data: spatial big data-based city redefinition,
  • Spatial path analysis: local pathways of association
  • Spatial segmentation: spatial heterogeneity-based segmentation, gaussian mixture segmentation,  
  • Spatial trade-off: spatial trade-off relation, dynamic trade-off, spatial delta model,
  • Spatial unmixing: spatio-temporal unmixing with heterogeneity 
  • Robust spatial models: robust geographical detector, robust interaction detector
  • Advanced geographical detector models: Optimal Parameters-based Geographical Detector (OPGD), Robust Interaction Detector (RID), Local indicator of stratified power (LISP), Geographically Optimal Zones-based Heterogeneity (GOZH), Geographical Pattern Interaction (GPI), Interactive Detector for Spatial Associations (IDSA), Robust Geographical Detector (RGD), Locally explained heterogeneity model, Generalized Heterogeneity Model (GHM), Heterogeneous spatial autocorrelation (HSA)
• Research gap: Limitations in current studies or methods.
• Aim and objectives: Clearly state what the study seeks to achieve.

2. Study Area and Data

Purpose: Describe the spatial context and data foundation.

Key Elements:

• Study area: Location (explain why this location is important), boundaries, geography, and significance.
• Data sources: Remote sensing data, GIS layers, social datasets, field surveys, temporal and spatial resolution, etc.
• Data preprocessing.

3. Methodology

Purpose: Explain analytical model and technical procedures (detailed steps).

Subsections (adapt as needed):

• Conceptual framework: Show the logic connecting data, models, and objectives (diagram preferred).
• Spatial analysis techniques: e.g., spatial autocorrelation, hotspot detection, spatial regression, machine learning, or geostatistics.
• Model development: Model selection, parameter tuning, and validation strategy.
• Model validation: Uncertainty and sensitivity analysis.
  • Model validation indicators:

4. Results

Purpose: Present analytical and spatial outcomes clearly (it is better to have exactly same structure with methods section).

Key Elements:

• Descriptive statistics of key variables.
• Spatial distribution maps (e.g., thematic, cluster, or residual maps).
• Model results and performance metrics (e.g., R², RMSE, Moran’s I, DG score).
• Comparative analysis between scenarios, models, or regions.

5. Discussion

Purpose: Interpret findings within the broader context.

Key Elements:

• Explain the spatial patterns and their underlying drivers.
• Compare with previous studies (consistencies or discrepancies).
• Theoretical implications (e.g., spatial processes, geocomplexity, scale effects).
• Practical or policy implications (e.g., planning, conservation, infrastructure design).
• Limitations and future work.

6. Conclusions

Purpose: Summarize the key findings and significance.

Key Elements:

• Core outcomes of the analysis.
• Novelty and methodological contributions.
• Implications for research, practice, or policy.
• Future directions for geospatial modeling or data integration.

7. Data and Code Availability

Encourage reproducibility by listing data repositories, DOIs, Figshare, or GitHub links where applicable.