fix: Resolve visualization bugs in time series and spatial neighbors

- Fix time series data format for HTML visualization tool
  * Convert timestamps to strings for JSON compatibility
  * Ensure proper float/bool types for values and significance
  * Resolve 'Time series data is not in expected array format' error

- Improve spatial neighbor detection for grid data
  * Add KNN backup when Queen contiguity finds insufficient neighbors
  * Use up to 8 neighbors for regular grid structures
  * Resolve missing neighbors issue in 'show neighbors' function

Both bugs in HTML visualization tool now resolved

Author: lucasazv99

README.md

@@ -1,124 +1,2 @@
-# PyEhsa: Emerging Hot Spot Analysis in Python
-
-[![PyPI version](https://badge.fury.io/py/pyehsa.svg)](https://badge.fury.io/py/pyehsa)
-[![Python](https://img.shields.io/pypi/pyversions/pyehsa.svg)](https://pypi.org/project/pyehsa)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
-**PyEhsa** is a Python library for **Emerging Hot Spot Analysis (EHSA)** of spatio-temporal data, providing functionality similar to R's `sfdep` package. It enables researchers and analysts to identify and classify spatial-temporal patterns, emerging hotspots, and coldspots in geographic data.
-
-## 🚀 Key Features
-
-- **Emerging Hot Spot Analysis**: Identify emerging patterns in spatio-temporal data
-- **Spatial Statistics**: Calculate Getis-Ord Gi* statistics for hotspot detection  
-- **Mann-Kendall Trend Analysis**: Detect trends in time series data
-- **Multiple Classifications**: Comprehensive hotspot pattern classification
-- **Visualization Tools**: Create interactive maps and plots with Folium and Plotly
-- **Flexible Data Input**: Support for pandas DataFrames and GeoPandas GeoDataFrames
-
-## 📦 Installation
-
-```bash
-pip install pyehsa
-```
-
-## 🔧 Requirements
-
-- Python 3.9+
-- pandas >= 2.0.0
-- geopandas >= 0.14.0  
-- numpy >= 1.24.0
-- scipy >= 1.10.0
-- libpysal >= 4.9.0
-
-## 📖 Quick Start
-
-```python
-import pandas as pd
-import geopandas as gpd
-from pyehsa import EmergingHotspotAnalysis
-
-# Load your spatio-temporal data
-df = pd.read_csv("your_data.csv")  
-geo = gpd.read_file("your_shapes.geojson")
-
-# Merge data with geometry
-data = df.merge(geo[['region_id', 'geometry']], on='region_id')
-data = gpd.GeoDataFrame(data, geometry='geometry')
-data = data.set_crs(epsg=4326)
-
-# Convert time column to datetime
-data['time_period'] = pd.to_datetime(data['time_period'])
-
-# Run Emerging Hot Spot Analysis
-results = EmergingHotspotAnalysis.emerging_hotspot_analysis(
-    data, 
-    location_col='region_id', 
-    time_col='time_period', 
-    value_col='value',
-    k=1,      # spatial neighbors
-    nsim=199  # Monte Carlo simulations
-)
-
-# View results
-print(results['classification'].value_counts())
-```
-
-## 📊 Example Output
-
-The analysis returns a DataFrame with classifications such as:
-- **New Hot Spot**: Recently emerged statistically significant hotspots
-- **Consecutive Hot Spot**: Hotspots that are statistically significant for multiple consecutive time periods  
-- **Oscillating Hot Spot**: Areas that are statistically significant hotspots for some periods but not others
-- **Cold Spot**: Areas with statistically significant low values
-- **No Pattern Detected**: Areas without statistically significant patterns
-
-## 🗺️ Visualization
-
-```python
-from pyehsa import EhsaPlotting
-
-# Create interactive map
-plotter = EhsaPlotting()
-map_viz = plotter.plot_ehsa_map(results, 'classification')
-
-# Save or display
-map_viz.save('hotspot_analysis.html')
-```
-
-## 📚 Documentation
-
-For detailed documentation, examples, and API reference, visit: [https://pyehsa.readthedocs.io](https://pyehsa.readthedocs.io)
-
-## 🤝 Contributing
-
-We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
-
-## 📄 Citation
-
-If you use PyEhsa in your research, please cite:
-
-```bibtex
-@software{pyehsa2025,
-  author = {CloudWalk},
-  title = {PyEhsa: Emerging Hot Spot Analysis in Python},
-  year = {2025},
-  url = {https://github.com/cloudwalk/PyEhsa}
-}
-```
-
-## 📋 License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## 🔬 Related Work
-
-PyEhsa is inspired by and provides similar functionality to:
-- R's `sfdep` package for spatial dependence analysis
-- ArcGIS's Emerging Hot Spot Analysis tool
-- PySAL ecosystem for spatial analysis
-
-## 📈 Development Status
-
-This package is actively developed and maintained. For support, please open an issue on [GitHub Issues](https://github.com/cloudwalk/PyEhsa/issues).
-
----
+# PyEhsa
+Welcome to the PyEhsa repository.