geoskill-land-value-estimation

土地价值估算 | Land Value Estimation

Estimates the spatial distribution of land value with a hedonic price model, supporting land price assessment and location analysis.

Core algorithm: accessibility = exp(−distance to center / decay scale), higher closer to the center; green proximity works the same way; value = intercept + β_acc × accessibility + β_poi × POI density + β_green × green proximity. The model is linear and additive with non-negative values; it ships with a least-squares coefficient calibration function that can exactly recover known coefficients from samples.

Dependencies / 依赖

pip install 'numpy' 'rasterio'

Usage / 使用方法

Basic usage

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 [other parameters]

Examples

Example 1 (synthetic data (offline))

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 2 (usage 2)

python geoskill-land-value-estimation.py --input features.tif --output-dir ./out

Example 3 (usage 3)

python geoskill-land-value-estimation.py --bbox 121.0 31.0 122.0 32.0 --decay 50 --output-dir ./out --quiet

Example 4 (usage 4)

python geoskill-land-value-estimation.py --input features.tif --coef-acc 8000 --intercept 2000 --output-dir ./out

Example 5 (usage 5)

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --coef-poi 3000 --output-dir ./out --quiet

Output / 输出

Data Source / 数据源 / Source

A local three-band feature GeoTIFF (distance to center, POI density, distance to green space); --synthetic mode simulates a monocentric urban pattern.

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is done locally; no user data is uploaded.

License / License

MIT

---

---

name: geoskill-land-value-estimation description: 'Estimate land value with a hedonic price model driven by accessibility, POI density and green proximity.'

土地价值估算 | Land Value Estimation

用特征价格（Hedonic）模型估算土地价值空间分布，服务于地价评估与区位分析。

核心算法：可达性 = exp(−到中心距离/衰减尺度)，越靠近中心越高；绿地邻近性同理；价值 = 截距 + β_acc×可达性 + β_poi×POI密度 + β_green×绿地邻近性。模型线性可加、价值非负；附带最小二乘系数标定函数，可由样本精确恢复已知系数。

依赖

pip install 'numpy' 'rasterio'

使用方法

基本用法

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 [其他参数]

示例

示例 1（合成数据（离线））

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 2（用法 2）

python geoskill-land-value-estimation.py --input features.tif --output-dir ./out

示例 3（用法 3）

python geoskill-land-value-estimation.py --bbox 121.0 31.0 122.0 32.0 --decay 50 --output-dir ./out --quiet

示例 4（用法 4）

python geoskill-land-value-estimation.py --input features.tif --coef-acc 8000 --intercept 2000 --output-dir ./out

示例 5（用法 5）

python geoskill-land-value-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --coef-poi 3000 --output-dir ./out --quiet

输出

数据源 / Source

本地三波段特征 GeoTIFF（到中心距离、POI 密度、到绿地距离）；--synthetic 模式模拟单中心城市格局。

隐私声明 / Privacy

• 默认离线运行，--synthetic 模式完全无网络。
• 所有处理在本地完成，不上传用户数据。

License

MIT