carto-routing-od-analysis

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Original

English

🇨🇳

Translation

Chinese

Routing and Origin-Destination Analysis

路径规划与起讫点（OD）分析

Builds CARTO Workflows that compute routes, travel time/distance matrices, and isoline catchment areas. Supports driving and walking modes. Also covers OD flow pattern analysis using spatial indexing.

Prerequisites: Load

carto-create-workflow

for the development process, JSON structure, and validation commands.

构建可计算路线、出行时间/距离矩阵以及等值线服务区的CARTO工作流。支持驾车和步行模式，还涵盖基于空间索引的OD流量模式分析。

前提条件：加载

carto-create-workflow

以获取开发流程、JSON结构和验证命令。

Instructions

操作说明

Three main workflow patterns exist. Choose based on the use case:

Pattern	Component	Use when
Isoline/Isochrone	`native.isolines`	You need catchment polygons around locations (e.g. "everywhere reachable within 10 min")
OD Matrix	`native.routesodmatrix`	You need travel time/distance between every origin-destination pair (analytics, no geometry)
Route Creation	`native.routes`	You need actual route line geometries between OD pairs (visualization, detailed path)

存在三种主要工作流模式，请根据使用场景选择：

模式	组件	使用场景
等值线/等时线	`native.isolines`	需要生成位置周边的服务区多边形（例如：“10分钟内可达的所有区域”）
OD矩阵	`native.routesodmatrix`	需要计算每一对起讫点之间的出行时间/距离（仅分析，无需几何图形）
路线生成	`native.routes`	需要起讫点对之间的实际路线几何图形（用于可视化、详细路径展示）

Pattern A: Isoline/Isochrone Generation

模式A：等值线/等时线生成

Pipeline:

Source Points -> (Filter) -> Isolines -> (Polyfill / Enrich) -> Save

流程：

源点数据 -> (过滤) -> 等值线生成 -> (Polyfill / 数据丰富) -> 保存

Step A1: Load Source Points

步骤A1：加载源点数据

Use

native.gettablebyname

to load locations (stores, stations, facilities).

Success: Table with a geometry column and a unique location identifier.

使用

native.gettablebyname

加载位置数据（门店、站点、设施等）。

成功标志：包含几何列和唯一位置标识符的表格。

Step A2: Generate Isolines

步骤A2：生成等值线

Use

native.isolines

with:

Input	Description	Example
`mode`	Travel mode	`car` or `walk`
`range_type`	What the range measures	`time` or `distance`
`range`	Threshold value	Seconds for time (e.g. `600` = 10 min), meters for distance (e.g. `5000` = 5 km)

Success: Each input point has an associated polygon geometry representing the reachable area.

使用

native.isolines

，配置参数如下：

输入项	描述	示例
`mode`	出行模式	`car` 或 `walk`
`range_type`	范围度量类型	`time` 或 `distance`
`range`	阈值	时间单位为秒（例如 `600` =10分钟），距离单位为米（例如 `5000` =5公里）

成功标志：每个输入点都对应一个代表可达区域的多边形几何图形。

Step A3: Post-Processing (optional)

步骤A3：后处理（可选）

Common follow-ups after isoline generation:

Polyfill + Enrich: Convert isoline polygons to H3 with
```
native.h3polyfill
```
, then enrich with demographics or POI data (see trade-area-analysis skill).
Overlap analysis: Use
```
native.spatialjoin
```
to find which isolines overlap, identifying areas served by multiple locations.
Coverage union: Use
```
native.dissolve
```
to merge all isoline polygons into a single coverage footprint.

等值线生成后的常见后续操作：

Polyfill + 数据丰富：使用
```
native.h3polyfill
```
将等值线多边形转换为H3网格，然后结合人口统计或POI数据进行丰富（参考商圈分析技能）。
重叠分析：使用
```
native.spatialjoin
```
查找重叠的等值线，识别被多个位置覆盖的区域。
覆盖范围合并：使用
```
native.dissolve
```
将所有等值线多边形合并为单个覆盖范围。

Step A4: Save

步骤A4：保存

Use

native.saveastable

to persist isoline polygons or enriched results.

Success: Validated workflow uploadable via

carto workflows create

使用

native.saveastable

保存等值线多边形或丰富后的结果。

成功标志：验证通过的工作流可通过

carto workflows create

上传。

Pattern B: OD Matrix (Travel Time/Distance)

模式B：OD矩阵（出行时间/距离）

Pipeline:

Origins Table -> ┐
                 ├-> OD Matrix -> (Filter/Aggregate) -> Save
Destinations Table -> ┘

流程：

起点表 -> ┐
          ├-> OD矩阵生成 -> (过滤/聚合) -> 保存
终点表 -> ┘

Step B1: Load Origins and Destinations

步骤B1：加载起点和终点数据

Use two

native.gettablebyname

nodes -- one for origins, one for destinations. Both need geometry columns.

Success: Two tables, each with point geometries and unique identifiers.

使用两个

native.gettablebyname

节点——一个加载起点数据，一个加载终点数据。两者都需要包含几何列。

成功标志：两个包含点几何图形和唯一标识符的表格。

Step B2: Compute OD Matrix

步骤B2：计算OD矩阵

Use

native.routesodmatrix

with:

Input	Description
`mode`	`car` or `walk`
Origins input	Connected from the origins table node
Destinations input	Connected from the destinations table node

Output columns:

origin_id

destination_id

duration_s

distance_m

Success: One row per origin-destination pair with travel time and distance.

使用

native.routesodmatrix

，配置参数如下：

输入项	描述
`mode`	`car` 或 `walk`
起点输入	连接到起点表节点
终点输入	连接到终点表节点

输出列：

origin_id

、

destination_id

、

duration_s

、

distance_m

。

成功标志：每一对起讫点对应一行数据，包含出行时间和距离。

Step B3: Filter or Aggregate (optional)

步骤B3：过滤或聚合（可选）

Common post-processing:

Nearest destination: Use
```
native.groupby
```
to find the minimum
```
duration_s
```
per origin, then join back to get the nearest destination.
Threshold filter: Use
```
native.where
```
to keep only pairs within a time/distance limit (e.g.
```
duration_s < 1800
```
for 30-min threshold).
Accessibility score: Count destinations reachable within a threshold per origin using
```
native.groupby
```
.

常见后处理操作：

最近终点：使用
```
native.groupby
```
找到每个起点对应的最小
```
duration_s
```
，然后关联回数据获取最近的终点。
阈值过滤：使用
```
native.where
```
保留时间/距离在阈值内的配对（例如
```
duration_s < 1800
```
即30分钟阈值）。
可达性评分：使用
```
native.groupby
```
统计每个起点在阈值内可达的终点数量。

Step B4: Save

步骤B4：保存

Use

native.saveastable

Success: Validated workflow uploadable via

carto workflows create

使用

native.saveastable

保存结果。

成功标志：验证通过的工作流可通过

carto workflows create

上传。

Pattern C: Route Geometries

模式C：路线几何图形

Pipeline:

Origins Table -> ┐
                 ├-> Routes -> Save
Destinations Table -> ┘

流程：

起点表 -> ┐
          ├-> 路线生成 -> 保存
终点表 -> ┘

Step C1: Load Origins and Destinations

步骤C1：加载起点和终点数据

Same as Pattern B -- two

native.gettablebyname

nodes with point geometries.

与模式B相同——两个

native.gettablebyname

节点，包含点几何图形。

Step C2: Compute Routes

步骤C2：计算路线

Use

native.routes

with:

Input	Description
`mode`	`car` or `walk`
Origins input	Connected from the origins table node
Destinations input	Connected from the destinations table node

Output: Route line geometries with

duration_s

and

distance_m

attributes.

Success: One route geometry per OD pair, visualizable on a map.

使用

native.routes

，配置参数如下：

输入项	描述
`mode`	`car` 或 `walk`
起点输入	连接到起点表节点
终点输入	连接到终点表节点

输出：包含

duration_s

和

distance_m

属性的路线几何图形。

成功标志：每一对起讫点对应一条路线几何图形，可在地图上可视化展示。

Step C3: Save

步骤C3：保存

Use

native.saveastable

Success: Validated workflow uploadable via

carto workflows create

使用

native.saveastable

保存结果。

成功标志：验证通过的工作流可通过

carto workflows create

上传。

Pattern D: OD Flow Analysis (Grid-Based)

模式D：OD流量分析（基于网格）

For analyzing trip/movement patterns at scale (e.g. taxi trips, bike rides, commute flows) without calling routing APIs.

Pipeline:

Trip Data -> H3 (origin) + H3 (destination) -> Group By (origin_h3, dest_h3) -> Save

用于大规模分析出行/移动模式（例如出租车行程、骑行、通勤流量），无需调用路径规划API。

流程：

出行数据 -> H3（起点） + H3（终点） -> 分组（origin_h3, dest_h3） -> 保存

Step D1: Load Trip Data

步骤D1：加载出行数据

Use

native.gettablebyname

. The table should have both origin and destination coordinates (e.g. pickup_lon/lat, dropoff_lon/lat).

使用

native.gettablebyname

加载数据。表格需包含起点和终点坐标（例如上车经纬度、下车经纬度）。

Step D2: Index Origins and Destinations to H3

步骤D2：将起点和终点索引到H3网格

Use

native.selectexpression

to compute H3 cells for both origin and destination points:

Origin H3: derive from pickup coordinates
Destination H3: derive from dropoff coordinates

Alternatively, if the data has separate geometry columns, use

native.h3frompoint

for each.

使用

native.selectexpression

为起点和终点计算H3网格单元：

起点H3：由上车坐标推导
终点H3：由下车坐标推导

若数据已包含单独的几何列，可分别使用

native.h3frompoint

计算。

Step D3: Aggregate Flows

步骤D3：聚合流量

Use

native.groupby

to count trips per (origin_h3, destination_h3) pair:

Group by:
```
origin_h3, destination_h3
```
Aggregation:
```
origin_h3,count
```
(trip count per OD pair)

Success: One row per unique OD cell pair with trip count -- ready for flow visualization.

使用

native.groupby

按（origin_h3, destination_h3）配对统计出行次数：

分组依据：
```
origin_h3, destination_h3
```
聚合方式：
```
origin_h3,count
```
（每对OD的出行次数）

成功标志：每个唯一OD网格配对对应一行数据，包含出行次数——可用于流量可视化。

Step D4: Save

步骤D4：保存

Use

native.saveastable

使用

native.saveastable

保存结果。

Gotchas

注意事项

Provider casing & SQL dialect. This skill uses lowercase column names (
```
origin_id
```
,
```
destination_id
```
,
```
duration_s
```
,
```
distance_m
```
,
```
origin_h3
```
, etc.) — BigQuery / Databricks / Postgres / Redshift convention. On Snowflake, reference these UPPERCASE (
```
ORIGIN_ID
```
,
```
DURATION_S
```
, ...). See
```
carto-create-workflow/references/providers/<provider>.md
```
for casing rules and SQL dialect equivalents.
Isolines and routing components consume LDS (Location Data Services) quota. Check available quota with
```
LDS_QUOTA_INFO
```
before bulk operations. Buffers (
```
native.buffer
```
) do not consume LDS quota and are a free alternative for simple circular catchments.
OD matrices grow quadratically: N origins x M destinations = N*M rows. Filter or sample inputs to keep the matrix manageable. For 1000 origins x 1000 destinations, you get 1 million rows.
Walking mode has a much shorter practical range than driving. Walking isolines beyond 20-30 minutes or OD matrices beyond a few kilometers produce unreliable or empty results.
Route geometries can be large. For pure analytics (time/distance only), prefer the OD matrix (Pattern B) over full routes (Pattern C) to reduce data volume.
Time-of-day affects driving results due to congestion. Specify
```
departure_time
```
if the component supports it; otherwise results reflect typical/average conditions.
Isoline polygons may overlap for nearby locations. If enriching afterwards, polyfill to a spatial index and deduplicate cells to avoid double-counting.
For OD flow visualization (Pattern D), use H3 cell center points rather than raw coordinates for cleaner aggregation and visualization. A coarser resolution (e.g. H3 res 7-8) produces more meaningful flow patterns than fine resolutions.
The LDS routing components require a connection with LDS API access enabled. Validation may fail if the connection lacks this permission.

提供商大小写与SQL方言：本技能使用小写列名（
```
origin_id
```
、
```
destination_id
```
、
```
duration_s
```
、
```
distance_m
```
、
```
origin_h3
```
等）——符合BigQuery / Databricks / Postgres / Redshift的约定。在Snowflake上，需引用大写列名（
```
ORIGIN_ID
```
、
```
DURATION_S
```
等）。查看
```
carto-create-workflow/references/providers/<provider>.md
```
获取大小写规则和SQL方言对应关系。
等值线和路径规划组件会消耗LDS（位置数据服务）配额。批量操作前请通过
```
LDS_QUOTA_INFO
```
检查可用配额。缓冲区（
```
native.buffer
```
）不消耗LDS配额，是简单圆形服务区的免费替代方案。
OD矩阵的规模呈二次增长：N个起点 × M个终点 = N*M行数据。请过滤或采样输入数据以控制矩阵规模。例如1000个起点 × 1000个终点会生成100万行数据。
步行模式的实际有效范围远小于驾车模式。步行等值线超过20-30分钟，或OD矩阵超过几公里范围时，结果可能不可靠或为空。
路线几何图形数据量较大。若仅需分析时间/距离，优先选择OD矩阵（模式B）而非完整路线（模式C）以减少数据量。
时段会影响驾车结果，因为存在交通拥堵。若组件支持，请指定
```
departure_time
```
；否则结果反映典型/平均路况。
邻近位置的等值线多边形可能重叠。若后续要进行数据丰富，需将多边形转换为空间索引并去重网格单元，避免重复统计。
对于OD流量可视化（模式D），使用H3网格中心点而非原始坐标进行聚合和可视化效果更佳。较粗的分辨率（例如H3第7-8级）比精细分辨率能生成更有意义的流量模式。
LDS路径规划组件需要启用LDS API访问权限的连接。若连接缺少该权限，验证可能失败。

Reference Templates

参考模板

Resource	Description
Scalable Routing Tutorial	Step-by-step scalable routing in Workflows
OD Patterns Tutorial	Analyzing origin-destination patterns (NYC taxi example)
Routing Module (BQ)	Using the routing module with Analytics Toolbox for BigQuery
Isoline Generation Template	Generating isochrones via Workflow templates
Trade Area Isolines (BQ)	Drive/walk-time isoline trade areas for BigQuery
Trade Area Isolines (SF)	Drive/walk-time isoline trade areas for Snowflake

资源	描述
Scalable Routing Tutorial	工作流中可扩展路径规划分析的分步教程
OD Patterns Tutorial	起讫点模式分析教程（纽约出租车示例）
Routing Module (BQ)	结合BigQuery分析工具箱使用路径规划模块
Isoline Generation Template	通过工作流模板生成等时线
Trade Area Isolines (BQ)	BigQuery中基于驾车/步行时间等值线的商圈生成
Trade Area Isolines (SF)	Snowflake中基于驾车/步行时间等值线的商圈生成

Common Variations

常见变体

Variant	How
Service area coverage	Isolines (car, multiple ranges e.g. 5/10/15 min) -> union -> measure total population covered
Nearest facility	OD matrix -> group by origin -> min(duration_s) -> join back to get nearest destination ID
Accessibility scoring	OD matrix -> filter by threshold -> count destinations per origin -> score by reachable count
Fleet route planning	Routes between depot and delivery points -> aggregate total distance/time per route
Commute flow analysis	Trip data -> H3 origin + H3 destination -> group by OD pair -> count -> visualize top flows
Multi-modal comparison	Run isolines twice (car + walk) -> compare coverage polygons -> identify transit-dependent areas

变体	实现方式
服务区域覆盖	生成等值线（驾车模式，多范围例如5/10/15分钟）-> 合并 -> 统计覆盖的总人口
最近设施查找	OD矩阵 -> 按起点分组 -> 取最小(duration_s) -> 关联获取最近终点ID
可达性评分	OD矩阵 -> 按阈值过滤 -> 统计每个起点可达的终点数量 -> 按可达数量评分
车队路线规划	生成仓库与配送点之间的路线 -> 聚合每条路线的总距离/时间
通勤流量分析	出行数据 -> H3起点 + H3终点 -> 按OD配对分组 -> 统计数量 -> 可视化Top流量
多模式对比	生成两次等值线（驾车+步行）-> 对比覆盖多边形 -> 识别依赖公共交通的区域