GIS MCP Server

Instructions

Implementation Reference

• src/gis_mcp/pysal_functions.py:335-393 (handler)

  Main handler for the 'join_counts' MCP tool. Loads shapefile, creates spatial weights, computes global binary join counts using esda.Join_Counts, and returns statistics including join counts, expected, variance, z-score, p-value, and data preview.
  @gis_mcp.tool() def join_counts(shapefile_path: str, dependent_var: str = "LAND_USE", target_crs: str = "EPSG:4326", distance_threshold: float = 100000) -> Dict[str, Any]: """Global Binary Join Counts.""" gdf, y, w, (threshold, unit), err = pysal_load_data(shapefile_path, dependent_var, target_crs, distance_threshold) if err: return {"status": "error", "message": err} # Join counts requires binary/categorical data - user must ensure y is binary (0/1 or True/False) import esda stat = esda.Join_Counts(y, w) preview = gdf[['geometry', dependent_var]].head(5).copy() preview['geometry'] = preview['geometry'].apply(lambda g: g.wkt) # Join_Counts attributes: J (total joins), bb, ww, bw, etc. join_count_val = None if hasattr(stat, "J"): join_count_val = float(stat.J) elif hasattr(stat, "jc"): join_count_val = float(stat.jc) elif hasattr(stat, "join_count"): join_count_val = float(stat.join_count) # Handle expected, variance, z_score - these might be DataFrames or scalars def safe_float(val): """Convert value to float, handling DataFrames and numpy types.""" if val is None: return None if isinstance(val, pd.DataFrame): # If it's a DataFrame, extract the first value return float(val.iloc[0, 0]) if not val.empty else None if isinstance(val, (np.ndarray, list, tuple)): return float(val[0]) if len(val) > 0 else None try: return float(val) except (ValueError, TypeError): return None expected_val = getattr(stat, "expected", None) variance_val = getattr(stat, "variance", None) z_score_val = getattr(stat, "z_score", None) p_val = None if hasattr(stat, "p_value"): p_val = safe_float(stat.p_value) elif hasattr(stat, "p_sim"): p_val = safe_float(stat.p_sim) return { "status": "success", "message": f"Join Counts completed successfully (threshold: {threshold} {unit})", "result": { "join_counts": join_count_val, "expected": safe_float(expected_val), "variance": safe_float(variance_val), "z_score": safe_float(z_score_val), "p_value": p_val, "data_preview": preview.to_dict(orient="records") } }
• src/gis_mcp/pysal_functions.py:108-134 (helper)

  Shared helper function called by 'join_counts' (and other ESDA tools) to load GeoDataFrame, validate inputs, reproject, compute effective distance threshold, create row-standardized DistanceBand weights, and handle islands by zeroing their weights.
  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
• src/gis_mcp/pysal_functions.py:13-28 (registration)

  MCP resource that lists available ESDA spatial analysis tools, including 'join_counts', discoverable via gis://operations/esda.
  @gis_mcp.resource("gis://operations/esda") def get_spatial_operations() -> Dict[str, List[str]]: """List available spatial analysis operations. This is for esda library. They are using pysal library.""" return { "operations": [ "getis_ord_g", "morans_i", "gearys_c", "gamma_statistic", "moran_local", "getis_ord_g_local", "join_counts", "join_counts_local", "adbscan" ] }