Ambee Global Pollen Data - Historical | Present | Forecast

**Overview**

Ambee's global pollen dataset provides hyper-local environmental intelligence on pollen, built for real-world decision systems across healthcare, consumer health, retail, and analytics platforms. The dataset is powered by a blend of environmental signals and proprietary modeling techniques, designed to reflect real-world conditions with some of the highest accuracy benchmarks in the industry.

**Use cases**

- Personalized digital health recommendations and allergy management
- Pharmaceutical demand forecasting for allergy medications
- Clinical workflow integration and patient care optimization
- Digital wellness platform development
- Allergy-aware routing and outdoor navigation
- Consumer health app integration
- Clinical trials efficiency and planning
- Climate-aware planning for outdoor events and activities

**Sample fields:**

- grass_pollen_count – Grass pollen count (particles/m³)
- grass_pollen – Risk level for grass pollen
- tree_pollen_count – Tree pollen count (particles/m³)
- tree_pollen – Risk level for tree pollen
- weed_pollen_count – Weed pollen count (particles/m³)
- weed_pollen – Risk level for weed pollen
- Species – Pollen count by individual subspecies (particles/m³) varies by region
- SpeciesRisk – Pollen risk level by individual subspecies (Low, Moderate, High, Very High), varies by region

**Subspecies covered:**
Acacia, Alder, Ash, Aster, Birch, Casuarina, Cedar, Chenopod, Cypress, Elm, Grass, Juniper, Maple, Mugwort, Mulberry, Myrtaceae, Nettle, Oak, Olive, Pine, Plane, Plantago, Poplar/Cottonwood, Ragweed, Rumex, Sedges, and Willow

**Tables included**

- Pollen_1hr_current_data
- Pollen_historical_data
- Pollen_historical_daily
- Pollen_forecast_120_hr_data
- Pollen_forecast_daily_30_days

**Benefits to Databricks Marketplace users**

- Instantly available: Delivered directly for immediate integration into any analytics, machine learning, or Databricks workflow.
- NAB-compliant risk scoring: Risk levels (Low, Moderate, High, Very High) following National Allergy Bureau guidelines for standardized pollen species assessment
- 30+ species and subspecies coverage: Includes category-level species such as Tree, Grass, and Weed, along with subspecies like Hazel, Elm, Pine, Alder, Oak, Birch, Cypress, Mugwort, Ragweed, Maple, Ash, and more.
- Historical depth + forecast horizon: Over a decade of pristine historical archives combined with industry-leading forecast data, enabling both historical analysis and forward-looking precision at scale.
- Hyperlocal precision: Data is provided on a scientifically consistent grid that can be mapped to any user-defined location or geography.

**概览**
Ambee公司的全球花粉数据集提供针对花粉的超本地化环境情报，专为医疗保健、消费者健康、零售及分析平台等领域的现实决策系统打造。该数据集融合环境信号与专有建模技术构建而成，旨在还原真实世界的环境状况，其精度基准处于行业顶尖水平之一。

**应用场景**
- 个性化数字健康推荐与过敏管理
- 过敏药物的药品需求预测
- 临床工作流集成与患者护理优化
- 数字健康平台开发
- 过敏友好路线规划与户外导航
- 消费者健康应用集成
- 临床试验效率提升与规划
- 户外赛事与活动的气候适配规划

**样本字段：**
- grass_pollen_count – 青草花粉浓度（颗粒/立方米）
- grass_pollen – 青草花粉风险等级
- tree_pollen_count – 树木花粉浓度（颗粒/立方米）
- tree_pollen – 树木花粉风险等级
- weed_pollen_count – 杂草花粉浓度（颗粒/立方米）
- weed_pollen – 杂草花粉风险等级
- Species – 按单个亚种划分的花粉浓度（颗粒/立方米），因地域而异
- SpeciesRisk – 按单个亚种划分的花粉风险等级（低、中、高、极高），因地域而异

**覆盖亚种：**
金合欢、桤木、白蜡树、紫菀、桦木、木麻黄、雪松、藜科植物、柏木、榆树、青草、刺柏、枫树、艾蒿、桑树、桃金娘科植物、荨麻、橡树、橄榄树、松树、悬铃木、车前草、杨树/三角叶杨、豚草、酸模、莎草以及柳树

**包含数据表**
- Pollen_1hr_current_data
- Pollen_historical_data
- Pollen_historical_daily
- Pollen_forecast_120_hr_data
- Pollen_forecast_daily_30_days

**面向Databricks Marketplace用户的优势**
- 即时可用：可直接交付并快速集成至任意分析、机器学习或Databricks工作流中。
- 符合NAB标准的风险评分：风险等级（低、中、高、极高）遵循美国国家过敏症局（National Allergy Bureau, NAB）的标准化花粉物种评估指南。
- 覆盖30余种物种及亚种：包含树木、青草、杂草等类别级物种，以及榛树、榆树、松树、桤木、橡树、桦木、柏木、艾蒿、豚草、枫树、白蜡树等亚种。
- 历史数据深度与预测视野：拥有超过十年的高质量历史档案，搭配行业领先的预测数据，可大规模支持历史分析与前瞻性精准决策。
- 超本地化精度：数据基于科学统一的网格提供，可映射至任意用户自定义的位置或地理区域。

搜集汇总

背景与挑战

背景概述

Ambee全球花粉数据集提供高精度的历史、实时和预测花粉数据，涵盖30多种花粉类型及其风险等级，适用于医疗健康、消费应用和气候规划等多个领域。数据集包含每小时、历史和预测数据表，支持即时集成和分析。