Ziyuan111/Urban_Tree_Canopy_in_Durham2

---
license: apache-2.0
language:
- en
size_categories:
- 10K<n<100K
---
# Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)

## Project Overview

The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.

## Background and Rationale

Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.

## Data Sources and Methodology

### Data Sources

We will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal:

- `GS_TreeInventory.shp`
- `Trees_&_Planting_Sites.csv`
- `Trees_%26_Planting_Sites.geojson`

# Dataset Card for Urban Tree Inventory

## Dataset Description

This dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services.

### Spatial Data (GeoJSON)

**Format:** GeoJSON

**Content:**

- **Type:** `FeatureCollection` - A collection of feature objects.
- **Features:** Each feature object represents a tree and contains:
- **Type:** `Feature`
- **Geometry:** `Point` (includes longitude and latitude of the tree location).
- **Properties:** Detailed information about the tree (some fields may overlap with the CSV structure below).

### Tabular Data (CSV)

**Format:** CSV

**Columns:**

- **X, Y:** Coordinates of the tree location.
- **OBJECTID:** Unique identifier for the tree.
- **streetaddress:** Street address nearest to the tree.
- **city:** City where the tree is located.
- **zipcode:** Zip code for the location of the tree.
- **facilityid:** Identifier for the facility associated with the tree, if any.
- **present:** Indication of whether the tree is currently present.
- **genus, species, commonname:** Botanical and common names of the tree.
- **plantingdate:** Date when the tree was planted.
- **diameterin:** Diameter of the tree trunk in inches.
- **heightft:** Height of the tree in feet.
- **condition:** Health condition of the tree.
- **contractwork:** Indicates if the tree has had any contract work done.
- **neighborhood:** Neighborhood where the tree is located.
- **program:** The program under which the tree was planted.
- **plantingw:** Width of the planting site.
- **plantingcond:** Condition of the planting site.
- **underpwerlins:** Whether the tree is under power lines.
- **matureheight:** The mature height of the tree.
- **GlobalID:** A global unique identifier for the tree.
- **created_user:** The user who created the record.
- **created_date:** The date the record was created.
- **last_edited_user:** The user who last edited the record.
- **last_edited_date:** The date the record was last edited.

#### Environmental and Economic Data:

- **isoprene, monoterpene, vocs:** Emissions and absorption data for various compounds.
- **coremoved_ozperyr, o3removed_ozperyr, etc.:** Annual pollutant removal metrics.
- **o2production_lbperyr:** Annual oxygen production.
- **carbonstorage_lb, carbonstorage_dol:** Carbon storage metrics.
- **grosscarseq_lbperyr, grosscarseq_dolperyr:** Gross carbon sequestration.
- **avoidrunoff_ft2peryr, avoidrunoff_dol2peryr:** Metrics related to stormwater runoff avoidance.
- **totannbenefits_dolperyr:** Total annual dollar benefits from the tree.
- **leafarea_sqft, potevapotran_cuftperyr, etc.:** Metrics related to the water cycle.
- **heating_mbtuperyr, cooling_kwhperyr, etc.:** Energy savings related to the tree's impact on building energy use.

### Example Record

**GeoJSON Feature:**
```json
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-78.90863, 36.00441]
},
"properties": {
"OBJECTID": 2840940,
"commonname": "Willow Oak",
// Additional properties...
}
}
```
The `GS_TreeInventory.shp` file encompasses a range of attributes for each record:

- **OBJECTID:** Unique identifier for each record.
- **streetaddr:** Street address where the tree or planting site is located.
- **city:** The city name, which is Durham.
- **zipcode:** Postal code for the location.
- **facilityid:** Identifier possibly linked to a facility or area associated with the tree.
- **present:** Type of feature present, such as a tree or a planting site.
- **genus:** Genus of the tree.
- **species:** Species of the tree.
- **commonname:** Common name of the tree.
- **plantingda:** Date or year range when the tree was planted or the planting site was established.
- ...
### Objectives

1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python.
2. Apply Python libraries to uncover relationships between tree canopy data and urban development.
3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy.
4. Produce analyses and visualizations with the GeoJSON file.

### Methodology

Our analytical process within Google Colab will encompass:

- **Data Preparation and Integration:** Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data.
- **Geospatial Analysis:** Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations.
- **Visualization and Optimization:** Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations.

## Deliverables

1. A collection of Google Colab Python notebooks that outline our analytical processes.
2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics.
3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy.

## Limitations

- **Computational Resources:** The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ.
- **Data Quality:** The accuracy and currency of the data ultimately affect the precision of our recommendations.
- **Sociopolitical Considerations:** Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input.

## Conclusion

DUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment.

原始信息汇总

数据集卡片 - 城市树木清单

数据集描述

该数据集提供了指定区域内城市树木的综合信息，包括它们的物理特征、环境效益以及在生态系统服务方面带来的经济价值。

空间数据 (GeoJSON)

格式： GeoJSON

内容：

• 类型： FeatureCollection - 一系列特征对象。
• 特征： 每个特征对象代表一棵树，包含：
  • 类型： Feature
  • 几何： Point（包含树木位置的经度和纬度）。
  • 属性： 树木的详细信息（某些字段可能与下面的CSV结构重叠）。

表格数据 (CSV)

格式： CSV

列：

• X, Y： 树木位置的坐标。
• OBJECTID： 树木的唯一标识符。
• streetaddress： 树木最近的街道地址。
• city： 树木所在的城市。
• zipcode： 树木位置的邮政编码。
• facilityid： 与树木相关的设施标识符（如果有）。
• present： 树木是否当前存在的指示。
• genus, species, commonname： 树木的属、种和通用名称。
• plantingdate： 树木种植的日期。
• diameterin： 树木树干的直径（英寸）。
• heightft： 树木的高度（英尺）。
• condition： 树木的健康状况。
• contractwork： 树木是否进行了合同工作。
• neighborhood： 树木所在的社区。
• program： 树木种植的项目。
• plantingw： 种植点的宽度。
• plantingcond： 种植点的状况。
• underpwerlins： 树木是否在电线下面。
• matureheight： 树木的成熟高度。
• GlobalID： 树木的全局唯一标识符。
• created_user： 创建记录的用户。
• created_date： 记录创建的日期。
• last_edited_user： 最后编辑记录的用户。
• last_edited_date： 记录最后编辑的日期。

环境与经济数据：

• isoprene, monoterpene, vocs： 各种化合物的排放和吸收数据。
• coremoved_ozperyr, o3removed_ozperyr, etc.： 年度污染物去除指标。
• o2production_lbperyr： 年度氧气生产。
• carbonstorage_lb, carbonstorage_dol： 碳储存指标。
• grosscarseq_lbperyr, grosscarseq_dolperyr： 总碳固存。
• avoidrunoff_ft2peryr, avoidrunoff_dol2peryr： 与避免雨水径流相关的指标。
• totannbenefits_dolperyr： 树木带来的年度总美元效益。
• leafarea_sqft, potevapotran_cuftperyr, etc.： 与水循环相关的指标。
• heating_mbtuperyr, cooling_kwhperyr, etc.： 与树木对建筑能源使用影响的能源节约相关指标。

示例记录

GeoJSON 特征： json { "type": "Feature", "geometry": { "type": "Point", "coordinates": [-78.90863, 36.00441] }, "properties": { "OBJECTID": 2840940, "commonname": "Willow Oak", // 其他属性... } }