GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:228-279 (handler)

  The primary handler function for the 'moran_local' MCP tool. It loads geospatial data, constructs spatial weights, handles isolated observations, computes Local Moran's I using esda.Moran_Local, and returns local I values, p-values, z-scores, and a data preview.
  @gis_mcp.tool() def moran_local(shapefile_path: str, dependent_var: str = "LAND_USE", target_crs: str = "EPSG:4326", distance_threshold: float = 100000) -> Dict[str, Any]: """Local Moran's I.""" gdf, y, w, (threshold, unit), err = pysal_load_data(shapefile_path, dependent_var, target_crs, distance_threshold) if err: return {"status": "error", "message": err} # Handle islands - if all points are islands, fall back to KNN weights for connectivity import libpysal if w.islands: if len(w.islands) == len(gdf): # All points are islands - fall back to KNN weights try: # Use k=4 for a 5x5 grid to ensure connectivity w = libpysal.weights.KNN.from_dataframe(gdf, k=4) w.transform = 'r' except Exception as e: return {"status": "error", "message": f"All units are islands and KNN fallback failed: {str(e)}"} else: # Some islands - filter them out keep_idx = [i for i in range(len(gdf)) if i not in set(w.islands)] if len(keep_idx) == 0: return {"status": "error", "message": "All units are islands (no neighbors). Try increasing distance_threshold."} # Filter data gdf_filtered = gdf.iloc[keep_idx].reset_index(drop=True) y_filtered = y[keep_idx] # Rebuild weights without islands using the same threshold w_filtered = libpysal.weights.DistanceBand.from_dataframe( gdf_filtered, threshold=threshold, # Use the effective threshold already calculated in pysal_load_data binary=False ) w_filtered.transform = 'r' gdf, y, w = gdf_filtered, y_filtered, w_filtered import esda stat = esda.Moran_Local(y, w) preview = gdf[['geometry', dependent_var]].head(5).copy() preview['geometry'] = preview['geometry'].apply(lambda g: g.wkt) # Return local statistics array summary return { "status": "success", "message": f"Local Moran's I completed successfully (threshold: {threshold} {unit})", "result": { "Is": stat.Is.tolist() if hasattr(stat.Is, 'tolist') else list(stat.Is), "p_values": stat.p_sim.tolist() if hasattr(stat.p_sim, 'tolist') else list(stat.p_sim), "z_scores": stat.z_sim.tolist() if hasattr(stat.z_sim, 'tolist') else list(stat.z_sim), "data_preview": preview.to_dict(orient="records") } }
• src/gis_mcp/pysal_functions.py:108-134 (helper)

  Shared helper function used by 'moran_local' and other PySAL tools to load GeoDataFrame, validate inputs, reproject, create row-standardized distance band spatial weights, and handle basic island cases.
  def pysal_load_data(shapefile_path: str, dependent_var: str, target_crs: str, distance_threshold: float): """Common loader and weight creation for esda statistics.""" if not os.path.exists(shapefile_path): return None, None, None, None, f"Shapefile not found: {shapefile_path}" gdf = gpd.read_file(shapefile_path) if dependent_var not in gdf.columns: return None, None, None, None, f"Dependent variable '{dependent_var}' not found in shapefile columns" gdf = gdf.to_crs(target_crs) effective_threshold = distance_threshold unit = "meters" if target_crs.upper() == "EPSG:4326": effective_threshold = distance_threshold / 111000 unit = "degrees" y = gdf[dependent_var].values.astype(np.float64) import libpysal w = libpysal.weights.DistanceBand.from_dataframe(gdf, threshold=effective_threshold, binary=False) w.transform = 'r' for island in w.islands: w.weights[island] = [0] * len(w.weights[island]) w.cardinalities[island] = 0 return gdf, y, w, (effective_threshold, unit), None