# Sample: Electricity utility, Houston: report

Project: Sample: Electricity utility, Houston  
Goal: Describe the project Sample: Electricity utility, Houston: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: 370b2dd2-5561-44b3-aa6f-5fe633bbe222 (running, 11 agent run(s))  
Generated: 2026-10-18T05:29:19Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Substations | vector, Point | 14 |  | 14 point features; 'voltage_kv' 35 to 69 (mean 64.14); 'capacity_mva' 40 to 120 (mean 68.57); 'commissioned' 1,970 to 2,017 (mean 1994); named by 'name' (e.g. Juniper Rise Substation, Kingsmead Substation, Larchfield Substation); extent -95.4070,29.7335 to -95.3261,29.7908. Point locations of Substations. | [98] |
| Feeder lines | vector, LineString | 79 |  | 79 line features; 'construction' has 2 values (top: overhead 46, underground 33); 'conductor' has 4 values (top: 1/0 ACSR 24, 477 AAC 21, 750 Cu UG 19); 'install_year' 1,964 to 2,022 (mean 1990); 'voltage_kv' 12.47 to 12.47 (mean 12.47); 'length_m' 540 to 3,063 (mean 1690); total length 133.5 km; extent -95.4215,29.7253 to -95.3181,29.7966. Lines (Feeder lines) such as routes or networks. | [99] |
| Service areas | vector, Polygon | 14 |  | 14 polygon (area) features; 'customers' 4,420 to 13,419 (mean 7965); 'peak_load_mw' 17.1 to 50.9 (mean 36.91); named by 'name' (e.g. Juniper Rise Substation area, Kingsmead Substation area, Larchfield Substation area); total area 79.96 km2; extent -95.4223,29.7234 to -95.3173,29.7974. Areas (Service areas) with customers to compare and map. | [100] |
| Outages | vector, Point | 1,650 | 2024-10-01 to 2026-09-30 | 1,650 point features; dated by 'started_at' from 2024-10-01 to 2026-09-30; 'cause' has 6 values (top: vegetation 644, equipment 427, weather 343); 'duration_min' 10 to 2,740 (mean 195); 'customers_affected' 6 to 3,173 (mean 234.9); extent -95.4212,29.7255 to -95.3187,29.7961. Dated point events by cause: where and when Outages happen. | [2] |
| Vegetation work orders | vector, Point | 480 | 2024-10-04 to 2026-09-21 | 480 point features; dated by 'date' from 2024-10-04 to 2026-09-21; 'trim_type' has 3 values (top: hazard tree 161, cycle trim 160, hot spot 159); 'spans' 1 to 12 (mean 6.692); 'cost_usd' 566 to 12,478 (mean 2667); extent -95.4210,29.7258 to -95.3186,29.7961. Dated point events by trim_type: where and when Vegetation work orders happen. | [3] |
| Critical customers | vector, Point | 60 |  | 60 point features; 'type' has 5 values (top: water plant 20, emergency shelter 11, fire station 10); 'backup_generator' has 2 values (top: False 39, True 21); 'priority' 1 to 3 (mean 1.683); extent -95.4198,29.7261 to -95.3218,29.7961. Point locations of Critical customers by type. | [101] |
| Poles | vector, Point | 450 |  | 450 point features; 'material' has 3 values (top: wood 207, concrete 131, steel 112); 'inspection_grade' has 4 values (top: B 187, D 95, A 87); 'install_year' 1,967 to 2,024 (mean 2001); extent -95.4214,29.7313 to -95.3201,29.7964. Point locations of Poles by material. | [102] |
| Feeder reliability by year | table | 316 | 2022-01-01 to 2025-01-01 | Table of 316 rows and 6 columns without geometry; dated by 'year' from 2022-01-01 to 2025-01-01; 'saidi_min' 35.2 to 486.9 (mean 121.6); 'saifi' 0.45 to 2.49 (mean 1.177); 'caidi_min' 29.3 to 391 (mean 106.5). A table of Feeder reliability by year without locations. | [4] |
| Transformer assets | table | 300 |  | Table of 300 rows and 6 columns without geometry; 'mount' has 2 values (top: pole 164, pad 136); 'kva' 25 to 167 (mean 83.32); 'install_year' 1,970 to 2,024 (mean 1997); 'condition_score' 1 to 10 (mean 5.301). A table of Transformer assets without locations. | [103] |
| Storm season reliability report | document |  |  | 1-page pdf document 'Storm season reliability report'; language en; mentions 3 place(s): Juniper Rise Substation (2), Kingsmead Substation (1), Larchfield Substation (1). A document about Juniper Rise Substation, Kingsmead Substation, Larchfield Substation. | [104] |
| Sample: Electricity utility, Houston | record |  |  |  | [105] |
| Emerging hot spots: Outages | layer, Polygon | 100 |  |  | [106] |
| Emerging hot spots: Vegetation work orders | layer, Polygon | 89 |  |  | [107] |
| Hot spot analysis: Poles | layer, Polygon | 79 |  |  | [108] |
| Hot spot analysis: Outages | layer, Point | 1,650 |  |  | [109] |
| Hot spot analysis: Vegetation work orders | layer, Polygon | 86 |  |  | [110] |
| Calculate density: Poles | layer, Polygon | 344 |  |  | [111] |
| Calculate density: Outages | layer, Polygon | 1,588 |  |  | [112] |
| Calculate density: Vegetation work orders | layer, Polygon | 350 |  |  | [113] |
| Summarize within: Service areas | layer, Polygon | 14 |  |  | [114] |
| Summarize within: Service areas | layer, Polygon | 14 |  |  | [115] |
| Summarize within: Service areas | layer, Polygon | 14 |  |  | [116] |
| Find point clusters: Poles | layer, Point | 450 |  |  | [117] |
| Find point clusters: cluster hulls | layer, Polygon | 12 |  |  | [118] |
| Find point clusters: Outages | layer, Point | 1,650 |  |  | [119] |
| Find point clusters: cluster hulls | layer, Polygon | 12 |  |  | [120] |
| Find point clusters: Vegetation work orders | layer, Point | 480 |  |  | [121] |
| Find point clusters: cluster hulls | layer, Polygon | 9 |  |  | [122] |
| Find outliers: Service areas | layer, Polygon | 14 |  |  | [123] |
| Repeat and near repeat classification: Outages | layer, Point | 1,650 |  |  | [124] |

## Timeline

![Timeline: events per quarter by dataset](charts/29-timeline-events-per-quarter-by-dataset.png)

*Figure 1. Timeline: events per quarter by dataset [1]. Per quarter, 2022-01-01 to 2026-07-01. Source: Outages, Vegetation work orders, Feeder reliability by year (2,446 features). Dotted lines mark dates named in the project's documents. The data is in the appendix.*

The timeline counts 2,446 dated events in Outages, Vegetation work orders, Feeder reliability by year per quarter, from Q1 2022 to Q3 2026 [1][2][3][4]. By dataset: Outages 1,650; Vegetation work orders 480; Feeder reliability by year 316 [1]. The trend is +19.6 events per quarter; the second half of the quarters has 2,209 against 237 in the first (+832.1%) [1]. 8 of the 19 quarters had no events [1]. The project's documents name 1 date inside this span, drawn as dotted lines; the most named is 2025-08-22 (Storm season reliability report, page 1, 1 mention) [5].

**Busiest quarters**

- Q3 2025: 425 events [1]
- Q3 2026: 310 events [1]
- Q1 2025: 290 events [1]

**Quietest quarters with events**

- Q1 2022: 79 events [1]
- Q1 2023: 79 events [1]
- Q1 2024: 79 events [1]

## Space time hot spots

**What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [6]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [6].

**Why it was run on this data.** Outages carry dates in 'started_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month [7].

**What it found.** The `gp_space_time_hot_spots` tool utilized the Outages input [9] and generated a result with 100 features [8].

| Result | Value |
| --- | --- |
| result, count | 100 |
| result, elapsed ms | 17 |
| result, resolution | 8 |
| result, features used | 1,650 |
| result, time neighbors | 0 |
| billing, cost | 0.1825 |
| billing, list price | 0.1825 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Outages: Emerging hot spots: 1 layer saved: Emerging hot spots: Outages (100 features) [8][9].
- **Evidenced:** It reported result, count 100; result, elapsed ms 17; result, resolution 8; result, features used 1,650; result, time neighbors 0; billing, cost 0.1825 [8].

*Tool gp_space_time_hot_spots; parameters {"agg": "sum", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "started_at", "value_field": "customers_affected"}*

## Space time hot spots

**What this analysis is.** Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time [6]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [6].

**Why it was run on this data.** Vegetation work orders carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per month [10].

**What it found.** Space time hot spots on Vegetation work orders: Emerging hot spots: 1 layer saved: Emerging hot spots: Vegetation work orders (89 features) [11][12]. It reported result, count 89; result, elapsed ms 12; result, resolution 8; result, features used 480; result, time neighbors 0; billing, cost 0.124 [11].

| Result | Value |
| --- | --- |
| result, count | 89 |
| result, elapsed ms | 12 |
| result, resolution | 8 |
| result, features used | 480 |
| result, time neighbors | 0 |
| billing, cost | 0.124 |
| billing, list price | 0.124 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Vegetation work orders: Emerging hot spots: 1 layer saved: Emerging hot spots: Vegetation work orders (89 features) [11][12].
- **Evidenced:** It reported result, count 89; result, elapsed ms 12; result, resolution 8; result, features used 480; result, time neighbors 0; billing, cost 0.124 [11].

*Tool gp_space_time_hot_spots; parameters {"project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Vegetation work orders (d3eba75a)"}, "interval": "month", "bin_shape": "h3", "resolution": 8, "time_field": "date"}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 450 points: Getis-Ord Gi* tests whether the concentrations in Poles are statistically significant [14].

**What it found.** Hot spots on Poles: Hot spot analysis: 1 layer saved: Hot spot analysis: Poles (79 features) [15][16]. It reported result, bins 79; result, count 79; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 450; result, distance band m 1,000 [15].

| Result | Value |
| --- | --- |
| result, bins | 79 |
| result, count | 79 |
| result, elapsed ms | 5 |
| result, cell size m | 1,000 |
| result, incidents used | 450 |
| result, distance band m | 1,000 |
| billing, cost | 0.059 |
| billing, list price | 0.059 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Poles: Hot spot analysis: 1 layer saved: Hot spot analysis: Poles (79 features) [15][16].
- **Evidenced:** It reported result, bins 79; result, count 79; result, elapsed ms 5; result, cell size m 1,000; result, incidents used 450; result, distance band m 1,000 [15].

*Tool gp_hot_spots; parameters {"project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Poles (cb7cbff1)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 1000.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 1650 points: Getis-Ord Gi* tests whether the concentrations in Outages are statistically significant [17].

**What it found.** The hot spot analysis used the Outages layer as the input data [9]. The analysis generated a total of 1,650 features, with the process completing in 27 ms [18].

| Result | Value |
| --- | --- |
| result, count | 1,650 |
| result, elapsed ms | 27 |
| result, excluded null | 0 |
| result, distance band m | 500 |
| billing, cost | 0.083 |
| billing, list price | 0.083 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Outages: Hot spot analysis: 1 layer saved: Hot spot analysis: Outages (1650 features) [9][18].
- **Evidenced:** It reported result, count 1,650; result, elapsed ms 27; result, excluded null 0; result, distance band m 500; billing, cost 0.083; billing, list price 0.083 [18].

*Tool gp_hot_spots; parameters {"field": "customers_affected", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 480 points: Getis-Ord Gi* tests whether the concentrations in Vegetation work orders are statistically significant [19].

**What it found.** Hot spots on Vegetation work orders: Hot spot analysis: 1 layer saved: Hot spot analysis: Vegetation work orders (86 features) [12][20]. It reported result, bins 86; result, count 86; result, elapsed ms 6; result, cell size m 1,000; result, incidents used 480; result, distance band m 1,000 [20].

| Result | Value |
| --- | --- |
| result, bins | 86 |
| result, count | 86 |
| result, elapsed ms | 6 |
| result, cell size m | 1,000 |
| result, incidents used | 480 |
| result, distance band m | 1,000 |
| billing, cost | 0.0596 |
| billing, list price | 0.0596 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Vegetation work orders: Hot spot analysis: 1 layer saved: Hot spot analysis: Vegetation work orders (86 features) [12][20].
- **Evidenced:** It reported result, bins 86; result, count 86; result, elapsed ms 6; result, cell size m 1,000; result, incidents used 480; result, distance band m 1,000 [20].

*Tool gp_hot_spots; parameters {"project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Vegetation work orders (d3eba75a)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 1000.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [21]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [21].

**Why it was run on this data.** 450 points in Poles: a density surface shows where they concentrate [22].

**What it found.** Calculate density on Poles: Calculate density: 1 layer saved: Calculate density: Poles (344 features) [16][23]. It reported result, count 344; result, elapsed ms 10; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 24.11 [23].

| Result | Value |
| --- | --- |
| result, count | 344 |
| result, elapsed ms | 10 |
| result, bandwidth m | 1,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 24.11 |
| billing, cost | 0.059 |
| billing, list price | 0.059 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Poles: Calculate density: 1 layer saved: Calculate density: Poles (344 features) [16][23].
- **Evidenced:** It reported result, count 344; result, elapsed ms 10; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 24.11 [23].

*A model draft was discarded because a sentence did not cite a source of this section.*

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Poles (cb7cbff1)"}, "bandwidth_m": 1000.0, "cell_size_m": 500.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [21]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [21].

**Why it was run on this data.** 1650 points in Outages: a density surface shows where they concentrate [24].

**What it found.** Calculate density on Outages: Calculate density: 1 layer saved: Calculate density: Outages (1588 features) [9][25]. It reported result, count 1,588; result, elapsed ms 45; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 4.538e+04 [25].

| Result | Value |
| --- | --- |
| result, count | 1,588 |
| result, elapsed ms | 45 |
| result, bandwidth m | 500 |
| result, cell size m | 200 |
| result, excluded null | 0 |
| result, max density per km2 | 4.538e+04 |
| billing, cost | 0.083 |
| billing, list price | 0.083 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Outages: Calculate density: 1 layer saved: Calculate density: Outages (1588 features) [9][25].
- **Evidenced:** It reported result, count 1,588; result, elapsed ms 45; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 4.538e+04 [25].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0, "weight_field": "customers_affected"}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [21]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [21].

**Why it was run on this data.** 480 points in Vegetation work orders: a density surface shows where they concentrate [26].

**What it found.** The maximum density calculated was 24.16 per km2 [27].

| Result | Value |
| --- | --- |
| result, count | 350 |
| result, elapsed ms | 10 |
| result, bandwidth m | 1,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 24.16 |
| billing, cost | 0.0596 |
| billing, list price | 0.0596 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Vegetation work orders: Calculate density: 1 layer saved: Calculate density: Vegetation work orders (350 features) [12][27].
- **Evidenced:** It reported result, count 350; result, elapsed ms 10; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 24.16 [27].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Vegetation work orders (d3eba75a)"}, "bandwidth_m": 1000.0, "cell_size_m": 500.0}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [28]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [28].

**Why it was run on this data.** Count Poles inside each feature of Service areas by material; the counts map as a choropleth (natural breaks, sequential colors) [29].

**What it found.** Summarize within on Poles, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [16][30][31]. It reported result, count 14; result, elapsed ms 1; billing, cost 0.0193; billing, list price 0.0193; billing, upstream cost 0 [30].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 1 |
| billing, cost | 0.0193 |
| billing, list price | 0.0193 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Poles, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [16][30][31].
- **Evidenced:** It reported result, count 14; result, elapsed ms 1; billing, cost 0.0193; billing, list price 0.0193; billing, upstream cost 0 [30].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "install_year"}], "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Poles (cb7cbff1)"}, "group_by": "material", "polygons": {"layer": "Service areas (af824cfb)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [28]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [28].

**Why it was run on this data.** Count Poles inside each feature of Service areas by material; the counts map as a choropleth (natural breaks, sequential colors) [29].

**What it found.** Summarize within on Outages, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [9][31][32]. It reported result, count 14; result, elapsed ms 3; billing, cost 0.0433; billing, list price 0.0433; billing, upstream cost 0 [32].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 3 |
| billing, cost | 0.0433 |
| billing, list price | 0.0433 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Outages, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [9][31][32].
- **Evidenced:** It reported result, count 14; result, elapsed ms 3; billing, cost 0.0433; billing, list price 0.0433; billing, upstream cost 0 [32].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "customers_affected"}], "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "group_by": "cause", "polygons": {"layer": "Service areas (af824cfb)"}}*

## Summarize within

**What this analysis is.** Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons [28]. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths [28].

**Why it was run on this data.** Count Poles inside each feature of Service areas by material; the counts map as a choropleth (natural breaks, sequential colors) [29].

**What it found.** Summarize within on Vegetation work orders, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [12][31][33]. It reported result, count 14; result, elapsed ms 1; billing, cost 0.0199; billing, list price 0.0199; billing, upstream cost 0 [33].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 1 |
| billing, cost | 0.0199 |
| billing, list price | 0.0199 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Vegetation work orders, Service areas: Summarize within: 1 layer saved: Summarize within: Service areas (14 features) [12][31][33].
- **Evidenced:** It reported result, count 14; result, elapsed ms 1; billing, cost 0.0199; billing, list price 0.0199; billing, upstream cost 0 [33].

*Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "spans"}], "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Vegetation work orders (d3eba75a)"}, "group_by": "trim_type", "polygons": {"layer": "Service areas (af824cfb)"}}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Poles and separates noise [35].

**What it found.** Find point clusters on Poles: Find point clusters: 2 layers saved: Find point clusters: Poles (450 features); Find point clusters: cluster hulls (12 features) [16][36]. It reported result, count 450; result, noise 149; result, distance m 500; result, elapsed ms 9; result, min points 11; billing, cost 0.059 [36].

| Result | Value |
| --- | --- |
| result, count | 450 |
| result, noise | 149 |
| result, distance m | 500 |
| result, elapsed ms | 9 |
| result, min points | 11 |
| billing, cost | 0.059 |
| billing, list price | 0.059 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Poles: Find point clusters: 2 layers saved: Find point clusters: Poles (450 features); Find point clusters: cluster hulls (12 features) [16][36].
- **Evidenced:** It reported result, count 450; result, noise 149; result, distance m 500; result, elapsed ms 9; result, min points 11; billing, cost 0.059 [36].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Poles (cb7cbff1)"}, "distance_m": 500.0, "min_points": 11}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Outages and separates noise [37].

**What it found.** Find point clusters on Outages: Find point clusters: 2 layers saved: Find point clusters: Outages (1650 features); Find point clusters: cluster hulls (12 features) [9][38]. It reported result, count 1,650; result, noise 985; result, distance m 200; result, elapsed ms 22; result, min points 20; billing, cost 0.083 [38].

| Result | Value |
| --- | --- |
| result, count | 1,650 |
| result, noise | 985 |
| result, distance m | 200 |
| result, elapsed ms | 22 |
| result, min points | 20 |
| billing, cost | 0.083 |
| billing, list price | 0.083 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Outages: Find point clusters: 2 layers saved: Find point clusters: Outages (1650 features); Find point clusters: cluster hulls (12 features) [9][38].
- **Evidenced:** It reported result, count 1,650; result, noise 985; result, distance m 200; result, elapsed ms 22; result, min points 20; billing, cost 0.083 [38].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "distance_m": 200.0, "min_points": 20}*

## Find point clusters

**What this analysis is.** Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise [34]. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering [34].

**Why it was run on this data.** DBSCAN finds dense groups of Vegetation work orders and separates noise [39].

**What it found.** Find point clusters on Vegetation work orders: Find point clusters: 2 layers saved: Find point clusters: Vegetation work orders (480 features); Find point clusters: cluster hulls (9 features) [12][40]. It reported result, count 480; result, noise 157; result, distance m 500; result, elapsed ms 9; result, min points 12; billing, cost 0.0596 [40].

| Result | Value |
| --- | --- |
| result, count | 480 |
| result, noise | 157 |
| result, distance m | 500 |
| result, elapsed ms | 9 |
| result, min points | 12 |
| billing, cost | 0.0596 |
| billing, list price | 0.0596 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Find point clusters on Vegetation work orders: Find point clusters: 2 layers saved: Find point clusters: Vegetation work orders (480 features); Find point clusters: cluster hulls (9 features) [12][40].
- **Evidenced:** It reported result, count 480; result, noise 157; result, distance m 500; result, elapsed ms 9; result, min points 12; billing, cost 0.0596 [40].

*Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Vegetation work orders (d3eba75a)"}, "distance_m": 500.0, "min_points": 12}*

## Outliers

**What this analysis is.** Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers [41]. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) [41].

**Why it was run on this data.** Local Moran's I maps clusters and outliers of 'customers' [42].

**What it found.** Outliers on Service areas: Find outliers: 1 layer saved: Find outliers: Service areas (14 features) [31][43]. It reported result, count 14; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 2,472; billing, cost 0.05042 [43].

| Result | Value |
| --- | --- |
| result, count | 14 |
| result, elapsed ms | 2 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,472 |
| billing, cost | 0.05042 |
| billing, list price | 0.05042 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Service areas: Find outliers: 1 layer saved: Find outliers: Service areas (14 features) [31][43].
- **Evidenced:** It reported result, count 14; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 2,472; billing, cost 0.05042 [43].

*Tool gp_outliers; parameters {"field": "customers", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Service areas (af824cfb)"}, "aggregation": "hexagon"}*

## Spatial autocorrelation

**What this analysis is.** Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random [44]. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) [44].

**Why it was run on this data.** Global Moran's I says whether similar values of 'customers' sit next to each other [45].

**What it found.** The spatial autocorrelation tool was run using Service areas [31], yielding a Moran's I of 0.02059 [46]. Analysis of this result showed a p value of 0.6627 [46] and a z score of 0.4361 [46]. Furthermore, the expected I value for the analysis was calculated to be -0.07692 [46].

| Result | Value |
| --- | --- |
| result, n | 14 |
| result, p value | 0.6627 |
| result, z score | 0.4361 |
| result, morans i | 0.02059 |
| result, variance | 0.04999 |
| result, elapsed ms | 1 |
| result, expected i | -0.07692 |
| result, excluded null | 0 |
| result, distance band m | 2,472 |
| result, p value normality | 0.671 |
| result, z score normality | 0.4248 |
| result, variance normality | 0.05269 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Spatial autocorrelation on Service areas: Spatial autocorrelation: done; no map output [31][46].
- **Evidenced:** It reported result, n 14; result, p value 0.6627; result, z score 0.4361; result, morans i 0.02059; result, variance 0.04999; result, elapsed ms 1 [46].

*Tool gp_spatial_autocorrelation; parameters {"field": "customers", "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Service areas (af824cfb)"}}*

## Near repeat

**What this analysis is.** Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table [47]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [47].

**Why it was run on this data.** Near repeat analysis tests whether Outages close in space also follow each other within 30 days [48].

**What it found.** Near repeat on Outages: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Outages (1650 features) [9][49]. It reported result, days 30; result, count 1,650; result, chains 266; result, distance m 200; result, elapsed ms 42; result, pairs linked 1,171 [49].

| Result | Value |
| --- | --- |
| result, days | 30 |
| result, count | 1,650 |
| result, chains | 266 |
| result, distance m | 200 |
| result, elapsed ms | 42 |
| result, pairs linked | 1,171 |
| billing, cost | 0.0995 |
| billing, list price | 0.0995 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Outages: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Outages (1650 features) [9][49].
- **Evidenced:** It reported result, days 30; result, count 1,650; result, chains 266; result, distance m 200; result, elapsed ms 42; result, pairs linked 1,171 [49].

*Tool gp_near_repeat; parameters {"days": 30, "project": "99e378b8-9e64-42f5-8930-fca7c2a73f57", "features": {"layer": "Outages (74249532)"}, "distance_m": 200.0, "time_field": "started_at"}*

## Maps and charts

The run also made 20 map image(s) [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] and 28 chart(s) [70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97].

![Emerging hot spots: Outages](maps/01-emerging-hot-spots-outages.png)

*Figure 2. Emerging hot spots: Outages [50]*

![Emerging hot spots: Vegetation work orders](maps/02-emerging-hot-spots-vegetation-work-orders.png)

*Figure 3. Emerging hot spots: Vegetation work orders [51]*

![Hot spot analysis: Poles](maps/03-hot-spot-analysis-poles.png)

*Figure 4. Hot spot analysis: Poles [52]*

![Hot spot analysis: Outages](maps/04-hot-spot-analysis-outages.png)

*Figure 5. Hot spot analysis: Outages [53]*

![Hot spot analysis: Vegetation work orders](maps/05-hot-spot-analysis-vegetation-work-orders.png)

*Figure 6. Hot spot analysis: Vegetation work orders [54]*

![Calculate density: Poles](maps/06-calculate-density-poles.png)

*Figure 7. Calculate density: Poles [55]*

![Calculate density: Outages](maps/07-calculate-density-outages.png)

*Figure 8. Calculate density: Outages [56]*

![Calculate density: Vegetation work orders](maps/08-calculate-density-vegetation-work-orders.png)

*Figure 9. Calculate density: Vegetation work orders [57]*

![Summarize within: Service areas](maps/09-summarize-within-service-areas.png)

*Figure 10. Summarize within: Service areas [58]*

![Summarize within: Service areas](maps/10-summarize-within-service-areas.png)

*Figure 11. Summarize within: Service areas [59]*

![Summarize within: Service areas](maps/11-summarize-within-service-areas.png)

*Figure 12. Summarize within: Service areas [60]*

![Find point clusters: Poles](maps/12-find-point-clusters-poles.png)

*Figure 13. Find point clusters: Poles [61]*

![Find point clusters: cluster hulls](maps/13-find-point-clusters-cluster-hulls.png)

*Figure 14. Find point clusters: cluster hulls [62]*

![Find point clusters: Outages](maps/14-find-point-clusters-outages.png)

*Figure 15. Find point clusters: Outages [63]*

![Find point clusters: cluster hulls](maps/15-find-point-clusters-cluster-hulls.png)

*Figure 16. Find point clusters: cluster hulls [64]*

![Find point clusters: Vegetation work orders](maps/16-find-point-clusters-vegetation-work-orders.png)

*Figure 17. Find point clusters: Vegetation work orders [65]*

![Find point clusters: cluster hulls](maps/17-find-point-clusters-cluster-hulls.png)

*Figure 18. Find point clusters: cluster hulls [66]*

![Service areas by customers](maps/18-service-areas-by-customers.png)

*Figure 19. Service areas by customers [67]*

![Find outliers: Service areas](maps/19-find-outliers-service-areas.png)

*Figure 20. Find outliers: Service areas [68]*

![Repeat and near repeat classification: Outages](maps/20-repeat-and-near-repeat-classification-outages.png)

*Figure 21. Repeat and near repeat classification: Outages [69]*

**What the map shows.** The map visualizes a hot spot analysis for vegetation work orders, classifying areas as having a pattern or having none. The majority of [51]

*Inferred: read from the image by gemma4:e4b, not evidence.*

![Feeder lines over time (install_year)](charts/01-feeder-lines-over-time-install-year.png)

*Figure 22. Feeder lines over time (install_year) [70]. Source: Feeder lines (79 features). The data is in the appendix.*

![Vegetation work orders over time (date)](charts/02-vegetation-work-orders-over-time-date.png)

*Figure 23. Vegetation work orders over time (date) [71]. Per month, 2024-10-01 to 2026-09-01. Source: Vegetation work orders (480 features). The data is in the appendix.*

![Poles over time (install_year)](charts/03-poles-over-time-install-year.png)

*Figure 24. Poles over time (install_year) [72]. Source: Poles (450 features). The data is in the appendix.*

![Feeder reliability by year over time (year)](charts/04-feeder-reliability-by-year-over-time-year.png)

*Figure 25. Feeder reliability by year over time (year) [73]. Per month, 2022-01-01 to 2025-01-01. Source: Feeder reliability by year (316 features). The data is in the appendix.*

![Transformer assets over time (install_year)](charts/05-transformer-assets-over-time-install-year.png)

*Figure 26. Transformer assets over time (install_year) [74]. Source: Transformer assets (300 features). The data is in the appendix.*

![Emerging hot spots: Outages](charts/06-emerging-hot-spots-outages.png)

*Figure 27. Emerging hot spots: Outages [75]. Source: Emerging hot spots: Outages (100 features). The data is in the appendix.*

![Emerging hot spots: Vegetation work orders](charts/07-emerging-hot-spots-vegetation-work-orders.png)

*Figure 28. Emerging hot spots: Vegetation work orders [76]. Source: Emerging hot spots: Vegetation work orders (89 features). The data is in the appendix.*

![Hot spot analysis: Poles](charts/08-hot-spot-analysis-poles.png)

*Figure 29. Hot spot analysis: Poles [77]. Source: Hot spot analysis: Poles (79 features). The data is in the appendix.*

![Hot spot analysis: Outages](charts/09-hot-spot-analysis-outages.png)

*Figure 30. Hot spot analysis: Outages [78]. Period: 2024-10-01 to 2026-09-30 (started_at). Source: Hot spot analysis: Outages (1,650 features). The data is in the appendix.*

![Hot spot analysis: Vegetation work orders](charts/10-hot-spot-analysis-vegetation-work-orders.png)

*Figure 31. Hot spot analysis: Vegetation work orders [79]. Source: Hot spot analysis: Vegetation work orders (86 features). The data is in the appendix.*

![Calculate density: Poles](charts/11-calculate-density-poles.png)

*Figure 32. Calculate density: Poles [80]. Source: Calculate density: Poles (344 features). The data is in the appendix.*

![Calculate density: Outages](charts/12-calculate-density-outages.png)

*Figure 33. Calculate density: Outages [81]. Source: Calculate density: Outages (1,588 features). The data is in the appendix.*

![Calculate density: Vegetation work orders](charts/13-calculate-density-vegetation-work-orders.png)

*Figure 34. Calculate density: Vegetation work orders [82]. Source: Calculate density: Vegetation work orders (350 features). The data is in the appendix.*

![Summarize within: Service areas](charts/14-summarize-within-service-areas.png)

*Figure 35. Summarize within: Service areas [83]. Source: Summarize within: Service areas (14 features). The data is in the appendix.*

![Summarize within: Service areas](charts/15-summarize-within-service-areas.png)

*Figure 36. Summarize within: Service areas [84]. Source: Summarize within: Service areas (14 features). The data is in the appendix.*

![Summarize within: Service areas](charts/16-summarize-within-service-areas.png)

*Figure 37. Summarize within: Service areas [85]. Source: Summarize within: Service areas (14 features). The data is in the appendix.*

![Find point clusters: Poles](charts/17-find-point-clusters-poles.png)

*Figure 38. Find point clusters: Poles [86]. Source: Find point clusters: Poles (450 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/18-find-point-clusters-cluster-hulls.png)

*Figure 39. Find point clusters: cluster hulls [87]. Source: Find point clusters: cluster hulls (12 features). The data is in the appendix.*

![Find point clusters: Outages](charts/19-find-point-clusters-outages.png)

*Figure 40. Find point clusters: Outages [88]. Period: 2024-10-01 to 2026-09-30 (started_at). Source: Find point clusters: Outages (1,650 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/20-find-point-clusters-cluster-hulls.png)

*Figure 41. Find point clusters: cluster hulls [89]. Source: Find point clusters: cluster hulls (12 features). The data is in the appendix.*

![Find point clusters: Vegetation work orders](charts/21-find-point-clusters-vegetation-work-orders.png)

*Figure 42. Find point clusters: Vegetation work orders [90]. Period: 2024-10-04 to 2026-09-21 (date). Source: Find point clusters: Vegetation work orders (480 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/22-find-point-clusters-cluster-hulls.png)

*Figure 43. Find point clusters: cluster hulls [91]. Source: Find point clusters: cluster hulls (9 features). The data is in the appendix.*

![Find outliers: Service areas](charts/23-find-outliers-service-areas.png)

*Figure 44. Find outliers: Service areas [92]. Source: Find outliers: Service areas (14 features). The data is in the appendix.*

![Repeat and near repeat classification: Outages](charts/24-repeat-and-near-repeat-classification-outages.png)

*Figure 45. Repeat and near repeat classification: Outages [93]. Per month, 2024-10-01 to 2026-09-01. Source: Repeat and near repeat classification: Outages (1,650 features). The data is in the appendix.*

![Repeat and near repeat classification: Outages over time](charts/25-repeat-and-near-repeat-classification-outages-over-time.png)

*Figure 46. Repeat and near repeat classification: Outages over time [94]. Per month, 2024-10-01 to 2026-09-01. Source: Repeat and near repeat classification: Outages (1,650 features). The data is in the appendix.*

![Find point clusters: Vegetation work orders over time](charts/26-find-point-clusters-vegetation-work-orders-over-time.png)

*Figure 47. Find point clusters: Vegetation work orders over time [95]. Per month, 2024-10-01 to 2026-09-01. Source: Find point clusters: Vegetation work orders (480 features). The data is in the appendix.*

![Find point clusters: Outages over time](charts/27-find-point-clusters-outages-over-time.png)

*Figure 48. Find point clusters: Outages over time [96]. Per month, 2024-10-01 to 2026-09-01. Source: Find point clusters: Outages (1,650 features). The data is in the appendix.*

![Hot spot analysis: Outages over time](charts/28-hot-spot-analysis-outages-over-time.png)

*Figure 49. Hot spot analysis: Outages over time [97]. Per month, 2024-10-01 to 2026-09-01. Source: Hot spot analysis: Outages (1,650 features). The data is in the appendix.*

## Changes since the last run

This is the first report of this project.

## Sources

[1] Rendering: Chart: Timeline: events per quarter by dataset. 57 rows (id 4819230f-295d-49a7-99bf-6b7163fc6b06)  
[2] Dataset: Outages. vector, 1650 rows (id 39df98b3-8a4f-4af6-838d-170705e0bb48)  
[3] Dataset: Vegetation work orders. vector, 480 rows (id 3aea1163-22b4-4a72-88d7-bcae1ecdad23)  
[4] Dataset: Feeder reliability by year. table, 316 rows (id 887eeeea-0f49-451b-93f4-5d68b309bfad)  
[5] Document page: Storm season reliability report, page 1. 22 August 2025 (id 32e35a86-4090-49ec-8ae9-87f7b6be0d6b:1)  
[6] Catalog: Emerging hot spots (operation catalog, gp_space_time_hot_spots). Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time. ArcGIS: Emerging Hot Spot Analysis, Create Space Time Cube. (id gp_space_time_hot_spots)  
[7] Plan: Space time hot spots of Outages (the Start plan's candidate c_de6ee55db604). Outages carry dates in 'started_at': emerging hot spot analysis finds new, intensifying and fading hot spots per month. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_de6ee55db604)  
[8] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Outages (100 features). (id 7e39f2a8-86e1-4856-b7a7-61e069df845f:0)  
[9] Layer: Outages. 1,650 features (id 74249532-c045-44c9-a616-0fafb7c162a0)  
[10] Plan: Space time hot spots of Vegetation work orders (the Start plan's candidate c_90f82b668b6e). Vegetation work orders carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per month. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_90f82b668b6e)  
[11] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Vegetation work orders (89 features). (id 7e39f2a8-86e1-4856-b7a7-61e069df845f:1)  
[12] Layer: Vegetation work orders. 480 features (id d3eba75a-4345-496b-b3c1-cfad466d3d73)  
[13] Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). (id gp_hot_spots)  
[14] Plan: Hot spots of Poles (the Start plan's candidate c_8543237d7e01). 450 points: Getis-Ord Gi* tests whether the concentrations in Poles are statistically significant. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_8543237d7e01)  
[15] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Poles (79 features). (id dd7f4187-7acb-4060-a294-8f874d038e7b:0)  
[16] Layer: Poles. 450 features (id cb7cbff1-729f-45fd-bbbc-a31c0a2b6043)  
[17] Plan: Hot spots of Outages (the Start plan's candidate c_2edeaea7ec43). 1650 points: Getis-Ord Gi* tests whether the concentrations in Outages are statistically significant. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_2edeaea7ec43)  
[18] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Outages (1650 features). (id dd7f4187-7acb-4060-a294-8f874d038e7b:1)  
[19] Plan: Hot spots of Vegetation work orders (the Start plan's candidate c_63c929cc68f4). 480 points: Getis-Ord Gi* tests whether the concentrations in Vegetation work orders are statistically significant. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_63c929cc68f4)  
[20] Tool result: gp_hot_spots, step 2. Hot spot analysis: 1 layer saved: Hot spot analysis: Vegetation work orders (86 features). (id dd7f4187-7acb-4060-a294-8f874d038e7b:2)  
[21] Catalog: Calculate density (operation catalog, gp_calculate_density). Kernel density per square kilometer on a grid. ArcGIS: Calculate Density, Kernel Density. QGIS: native:heatmapkerneldensityestimation. (id gp_calculate_density)  
[22] Plan: Kernel density of Poles (the Start plan's candidate c_39401f3daf3b). 450 points in Poles: a density surface shows where they concentrate. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_39401f3daf3b)  
[23] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Poles (344 features). (id cddccd9e-fca2-4947-ac52-4a032f65ccd9:0)  
[24] Plan: Kernel density of Outages (the Start plan's candidate c_5f4ad3832af3). 1650 points in Outages: a density surface shows where they concentrate. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_5f4ad3832af3)  
[25] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Outages (1588 features). (id cddccd9e-fca2-4947-ac52-4a032f65ccd9:1)  
[26] Plan: Kernel density of Vegetation work orders (the Start plan's candidate c_fcff6300c7b4). 480 points in Vegetation work orders: a density surface shows where they concentrate. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_fcff6300c7b4)  
[27] Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Vegetation work orders (350 features). (id cddccd9e-fca2-4947-ac52-4a032f65ccd9:2)  
[28] Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. (id gp_summarize_within)  
[29] Plan: Poles per feature of Service areas (the Start plan's candidate c_0abb031a5451). Count Poles inside each feature of Service areas by material; the counts map as a choropleth (natural breaks, sequential colors). (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_0abb031a5451)  
[30] Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Service areas (14 features). (id 7abde2bd-08d7-47d0-ac4b-96744e82d444:0)  
[31] Layer: Service areas. 14 features (id af824cfb-eb9c-4c01-a5c2-df454a844893)  
[32] Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Service areas (14 features). (id 7abde2bd-08d7-47d0-ac4b-96744e82d444:1)  
[33] Tool result: gp_summarize_within, step 2. Summarize within: 1 layer saved: Summarize within: Service areas (14 features). (id 7abde2bd-08d7-47d0-ac4b-96744e82d444:2)  
[34] Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. (id gp_find_point_clusters)  
[35] Plan: Cluster points of Poles (the Start plan's candidate c_49349f7319f8). DBSCAN finds dense groups of Poles and separates noise. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_49349f7319f8)  
[36] Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Poles (450 features); Find point clusters: cluster hulls (12 features). (id a1a5ebf4-55fc-498c-bc42-232278d7fd50:0)  
[37] Plan: Cluster points of Outages (the Start plan's candidate c_9d082d10c718). DBSCAN finds dense groups of Outages and separates noise. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_9d082d10c718)  
[38] Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Outages (1650 features); Find point clusters: cluster hulls (12 features). (id a1a5ebf4-55fc-498c-bc42-232278d7fd50:1)  
[39] Plan: Cluster points of Vegetation work orders (the Start plan's candidate c_ec3997c2afdb). DBSCAN finds dense groups of Vegetation work orders and separates noise. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_ec3997c2afdb)  
[40] Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Vegetation work orders (480 features); Find point clusters: cluster hulls (9 features). (id a1a5ebf4-55fc-498c-bc42-232278d7fd50:2)  
[41] Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). (id gp_outliers)  
[42] Plan: Local morans i of Service areas (the Start plan's candidate c_14f3a517187f). Local Moran's I maps clusters and outliers of 'customers'. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_14f3a517187f)  
[43] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Service areas (14 features). (id 0eeb802a-0001-4240-800b-19f28e01e992:0)  
[44] Catalog: Spatial autocorrelation (operation catalog, gp_spatial_autocorrelation). Global Moran’s I: is the pattern clustered, dispersed or random. ArcGIS: Spatial Autocorrelation (Global Moran’s I). (id gp_spatial_autocorrelation)  
[45] Plan: Global morans i of Service areas (the Start plan's candidate c_ce02f9951480). Global Moran's I says whether similar values of 'customers' sit next to each other. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_ce02f9951480)  
[46] Tool result: gp_spatial_autocorrelation, step 1. Spatial autocorrelation: done; no map output. (id 0eeb802a-0001-4240-800b-19f28e01e992:1)  
[47] Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. (id gp_near_repeat)  
[48] Plan: Near repeat of Outages (the Start plan's candidate c_c8acd413fa13). Near repeat analysis tests whether Outages close in space also follow each other within 30 days. (id a5b6c9d4-e26f-4c51-adac-5b8547525805:c_c8acd413fa13)  
[49] Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Outages (1650 features). (id 7d19f92c-f4e9-48f3-93b0-dae3bf87b4ee:0)  
[50] Rendering: Map: Emerging hot spots: Outages (id 85c7e950-dc12-47d7-9698-4f3e33d102c5)  
[51] Rendering: Map: Emerging hot spots: Vegetation work orders (id d57a1eed-7833-4fa0-ba3b-1d34a14adf8d)  
[52] Rendering: Map: Hot spot analysis: Poles (id 9c171d35-6e8a-404e-b179-3a277cac6680)  
[53] Rendering: Map: Hot spot analysis: Outages (id 8b2915ad-3dc8-4c93-8998-6f8c60edde26)  
[54] Rendering: Map: Hot spot analysis: Vegetation work orders (id 1951c285-f42c-4fe7-a12b-c61e0f8e89b1)  
[55] Rendering: Map: Calculate density: Poles (id 77494a3a-e8bf-4ba9-8c9f-bbca3ed39699)  
[56] Rendering: Map: Calculate density: Outages (id 2539e062-6c7a-48d8-915c-ceb004cf1c30)  
[57] Rendering: Map: Calculate density: Vegetation work orders (id 90718fcc-1e9e-4189-b307-dd23cdc248d7)  
[58] Rendering: Map: Summarize within: Service areas (id aaf8c40e-6ba8-477d-9349-b080f65d9f6f)  
[59] Rendering: Map: Summarize within: Service areas (id 205c58ca-663e-465b-b5da-e58df3a3a282)  
[60] Rendering: Map: Summarize within: Service areas (id baad37d3-dcec-4a96-add7-fa1a886f6434)  
[61] Rendering: Map: Find point clusters: Poles (id 244f950b-322a-4f47-9af0-a30ad8fb8bad)  
[62] Rendering: Map: Find point clusters: cluster hulls (id d83dd986-dc80-4bfd-886c-ff9fb5202ae7)  
[63] Rendering: Map: Find point clusters: Outages (id a84598e8-6ff6-4f96-960b-18389e35387e)  
[64] Rendering: Map: Find point clusters: cluster hulls (id f19bdfd5-1091-44cd-baae-dbac01077b12)  
[65] Rendering: Map: Find point clusters: Vegetation work orders (id f99266a6-079b-44d5-852d-676355c69c8f)  
[66] Rendering: Map: Find point clusters: cluster hulls (id d61dc500-d849-4c67-8699-7c2b54d04ac2)  
[67] Rendering: Map: Service areas by customers (id 053475d3-62c9-4ca9-9150-1780c6628edd)  
[68] Rendering: Map: Find outliers: Service areas (id 40748e31-9982-4c38-83df-2e829b1cd052)  
[69] Rendering: Map: Repeat and near repeat classification: Outages (id 2dd79f43-b1db-44d1-b050-cb0ad8062f39)  
[70] Rendering: Chart: Feeder lines over time (install_year). 8 rows (id a6ddfbd0-4d56-4fd4-95e4-dccff9b3fe14)  
[71] Rendering: Chart: Vegetation work orders over time (date). 72 rows (id fe31e6f1-8de6-4739-b859-5ca5639a090e)  
[72] Rendering: Chart: Poles over time (install_year). 10 rows (id 1a338811-8c8f-4624-affb-5f6f2a386c21)  
[73] Rendering: Chart: Feeder reliability by year over time (year). 37 rows (id 81342282-782d-4f77-a6ab-c97a78fe8806)  
[74] Rendering: Chart: Transformer assets over time (install_year). 10 rows (id 8b1d0ff4-f186-4254-b010-9684ed1844c3)  
[75] Rendering: Chart: Emerging hot spots: Outages. 8 rows (id d57c5b66-d8b2-462a-85ae-76e9926eb3b1)  
[76] Rendering: Chart: Emerging hot spots: Vegetation work orders. 8 rows (id d126a2b6-196d-4f93-93d5-5ce7f5d9f891)  
[77] Rendering: Chart: Hot spot analysis: Poles. 9 rows (id b3100e77-5cbe-4a62-b253-290a5d536bb5)  
[78] Rendering: Chart: Hot spot analysis: Outages. 18 rows (id cec85df8-5d0a-4af6-ba88-696eca4ce437)  
[79] Rendering: Chart: Hot spot analysis: Vegetation work orders. 10 rows (id 1a614d2a-305a-4dfc-a43d-d303a95fb7b7)  
[80] Rendering: Chart: Calculate density: Poles. 10 rows (id 2ea4b4d0-a10d-4113-b8f2-fb2372244db4)  
[81] Rendering: Chart: Calculate density: Outages. 12 rows (id a201fc49-4d81-4320-8155-2f3eb69bd7da)  
[82] Rendering: Chart: Calculate density: Vegetation work orders. 10 rows (id 1a66e57b-240e-4644-ab5f-f5d3fceeebe6)  
[83] Rendering: Chart: Summarize within: Service areas. 5 rows (id ac78768b-6dc9-4d58-b972-2cf6dec976ea)  
[84] Rendering: Chart: Summarize within: Service areas. 5 rows (id 0ee3a30f-bde2-4bb4-9019-791dd822ac96)  
[85] Rendering: Chart: Summarize within: Service areas. 5 rows (id 1cd10d51-b513-49a7-b54f-6694f693c6a5)  
[86] Rendering: Chart: Find point clusters: Poles. 13 rows (id 4d716dc4-27b8-4ca0-8c8b-b4246f0cf77f)  
[87] Rendering: Chart: Find point clusters: cluster hulls. 12 rows (id d68e61ee-9527-48bd-93b1-e6d214930ddb)  
[88] Rendering: Chart: Find point clusters: Outages. 13 rows (id 658b2721-1008-4707-8089-5fc9e67e755d)  
[89] Rendering: Chart: Find point clusters: cluster hulls. 12 rows (id 3c8e6159-5e84-4f48-a0a3-71bc429bc627)  
[90] Rendering: Chart: Find point clusters: Vegetation work orders. 10 rows (id 77d5d6a0-286e-48b2-83b6-f1379a2b6e25)  
[91] Rendering: Chart: Find point clusters: cluster hulls. 9 rows (id ebdaf978-c973-42d9-b815-55c700b8af71)  
[92] Rendering: Chart: Find outliers: Service areas. 14 rows (id c9960c12-89e8-43ab-83aa-898a65c8d7e1)  
[93] Rendering: Chart: Repeat and near repeat classification: Outages. 144 rows (id 59506424-cd43-4c06-82b9-310f05aa3ef1)  
[94] Rendering: Chart: Repeat and near repeat classification: Outages over time. 144 rows (id e21c9039-2700-4091-9c70-031cd03ca122)  
[95] Rendering: Chart: Find point clusters: Vegetation work orders over time. 144 rows (id 17f450e3-64de-4bba-a8b9-1f01286c5da9)  
[96] Rendering: Chart: Find point clusters: Outages over time. 144 rows (id 7c632546-38e2-4ef7-a37f-a50b9e7200a4)  
[97] Rendering: Chart: Hot spot analysis: Outages over time. 144 rows (id 89489499-b4a2-4e58-b157-ba3c649aecd2)  
[98] Dataset: Substations. vector, 14 rows (id f259f42c-1706-41ba-a78a-1361f3914e06)  
[99] Dataset: Feeder lines. vector, 79 rows (id ecfb48ff-6d02-430b-9ebb-f111ab17967c)  
[100] Dataset: Service areas. vector, 14 rows (id f3d1d854-cd9c-48c7-8f7a-a2e854a8ceb6)  
[101] Dataset: Critical customers. vector, 60 rows (id e19506c9-69b8-4925-bb88-2c0235b75a70)  
[102] Dataset: Poles. vector, 450 rows (id 3786e568-d7a4-4d47-b594-453c79329fb0)  
[103] Dataset: Transformer assets. table, 300 rows (id 2e9131d9-123c-4a43-986c-82f9c9221692)  
[104] Dataset: Storm season reliability report. document, ? rows (id b535c355-1229-4d0f-9126-2fe58457bcec)  
[105] Record: Sample: Electricity utility, Houston (id 99e378b8-9e64-42f5-8930-fca7c2a73f57)  
[106] Layer: Emerging hot spots: Outages. 100 features (id c053bfd3-6311-4cf3-a811-669d5782bdb3)  
[107] Layer: Emerging hot spots: Vegetation work orders. 89 features (id 1cbdbfca-5f5d-4277-87ce-4d482b63ec8e)  
[108] Layer: Hot spot analysis: Poles. 79 features (id ddab229d-5eab-4322-8c2b-76442371347b)  
[109] Layer: Hot spot analysis: Outages. 1,650 features (id 152d5bad-10c2-42e3-98ef-f598b1d425bd)  
[110] Layer: Hot spot analysis: Vegetation work orders. 86 features (id aa682980-c78f-45bf-a39c-e6c25235ecf0)  
[111] Layer: Calculate density: Poles. 344 features (id 092a4943-ffb6-462a-afdb-cfca08b63795)  
[112] Layer: Calculate density: Outages. 1,588 features (id c9cfc32c-1601-4e5a-9160-a0a02112b586)  
[113] Layer: Calculate density: Vegetation work orders. 350 features (id c3b70581-64dc-4b78-82c2-af051c400b18)  
[114] Layer: Summarize within: Service areas. 14 features (id 080e40ba-f7ec-49b3-856b-2145df7839bf)  
[115] Layer: Summarize within: Service areas. 14 features (id 9f88918e-62a5-4499-b99b-d19d559ee0a7)  
[116] Layer: Summarize within: Service areas. 14 features (id a4302e84-efd6-417d-9791-39532ad03ea3)  
[117] Layer: Find point clusters: Poles. 450 features (id d8bd47c0-3090-477e-9f4e-99798bfd2778)  
[118] Layer: Find point clusters: cluster hulls. 12 features (id 99b9ab3e-1fc2-4630-afb7-f5a5db637828)  
[119] Layer: Find point clusters: Outages. 1,650 features (id 402ad5f6-a892-490a-a647-e6e94f591e27)  
[120] Layer: Find point clusters: cluster hulls. 12 features (id 56caa497-390e-42c8-9f4d-6813ec441e8c)  
[121] Layer: Find point clusters: Vegetation work orders. 480 features (id 9ab95317-7431-4636-b94c-06bb41924b25)  
[122] Layer: Find point clusters: cluster hulls. 9 features (id 134428d0-3eb6-4afb-b65a-2f63ca197338)  
[123] Layer: Find outliers: Service areas. 14 features (id 31a2a6ae-e1c2-4384-bdb2-7d30ebff9f80)  
[124] Layer: Repeat and near repeat classification: Outages. 1,650 features (id 9fd9267d-3aae-4cac-90a0-3de6c177fa4f)  

## Appendix: the data behind the charts

### Figure 1. Timeline: events per quarter by dataset [1]

| period | series | value |
| --- | --- | --- |
| 2022-01-01 | Outages | 0 |
| 2022-01-01 | Vegetation work orders | 0 |
| 2022-01-01 | Feeder reliability by year | 79 |
| 2022-04-01 | Outages | 0 |
| 2022-04-01 | Vegetation work orders | 0 |
| 2022-04-01 | Feeder reliability by year | 0 |
| 2022-07-01 | Outages | 0 |
| 2022-07-01 | Vegetation work orders | 0 |
| 2022-07-01 | Feeder reliability by year | 0 |
| 2022-10-01 | Outages | 0 |
| 2022-10-01 | Vegetation work orders | 0 |
| 2022-10-01 | Feeder reliability by year | 0 |
| 2023-01-01 | Outages | 0 |
| 2023-01-01 | Vegetation work orders | 0 |
| 2023-01-01 | Feeder reliability by year | 79 |
| 2023-04-01 | Outages | 0 |
| 2023-04-01 | Vegetation work orders | 0 |
| 2023-04-01 | Feeder reliability by year | 0 |
| 2023-07-01 | Outages | 0 |
| 2023-07-01 | Vegetation work orders | 0 |
| 2023-07-01 | Feeder reliability by year | 0 |
| 2023-10-01 | Outages | 0 |
| 2023-10-01 | Vegetation work orders | 0 |
| 2023-10-01 | Feeder reliability by year | 0 |
| 2024-01-01 | Outages | 0 |
| 2024-01-01 | Vegetation work orders | 0 |
| 2024-01-01 | Feeder reliability by year | 79 |
| 2024-04-01 | Outages | 0 |
| 2024-04-01 | Vegetation work orders | 0 |
| 2024-04-01 | Feeder reliability by year | 0 |
| 2024-07-01 | Outages | 0 |
| 2024-07-01 | Vegetation work orders | 0 |
| 2024-07-01 | Feeder reliability by year | 0 |
| 2024-10-01 | Outages | 163 |
| 2024-10-01 | Vegetation work orders | 45 |
| 2024-10-01 | Feeder reliability by year | 0 |
| 2025-01-01 | Outages | 151 |
| 2025-01-01 | Vegetation work orders | 60 |
| 2025-01-01 | Feeder reliability by year | 79 |
| 2025-04-01 | Outages | 160 |
| 2025-04-01 | Vegetation work orders | 79 |
| 2025-04-01 | Feeder reliability by year | 0 |
| 2025-07-01 | Outages | 384 |
| 2025-07-01 | Vegetation work orders | 41 |
| 2025-07-01 | Feeder reliability by year | 0 |
| 2025-10-01 | Outages | 185 |
| 2025-10-01 | Vegetation work orders | 48 |
| 2025-10-01 | Feeder reliability by year | 0 |
| 2026-01-01 | Outages | 168 |
| 2026-01-01 | Vegetation work orders | 72 |
| 2026-01-01 | Feeder reliability by year | 0 |
| 2026-04-01 | Outages | 176 |
| 2026-04-01 | Vegetation work orders | 88 |
| 2026-04-01 | Feeder reliability by year | 0 |
| 2026-07-01 | Outages | 263 |
| 2026-07-01 | Vegetation work orders | 47 |
| 2026-07-01 | Feeder reliability by year | 0 |

### Figure 22. Feeder lines over time (install_year) [70]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1,964 | 1,971 | 12 |
| 1,971 | 1,978 | 16 |
| 1,978 | 1,986 | 10 |
| 1,986 | 1,993 | 6 |
| 1,993 | 2,000 | 14 |
| 2,000 | 2,008 | 10 |
| 2,008 | 2,015 | 2 |
| 2,015 | 2,022 | 9 |

### Figure 23. Vegetation work orders over time (date) [71]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | hazard tree | 2 |
| 2024-10-01 | cycle trim | 4 |
| 2024-10-01 | hot spot | 5 |
| 2024-11-01 | hazard tree | 5 |
| 2024-11-01 | cycle trim | 4 |
| 2024-11-01 | hot spot | 3 |
| 2024-12-01 | hazard tree | 9 |
| 2024-12-01 | cycle trim | 6 |
| 2024-12-01 | hot spot | 7 |
| 2025-01-01 | hazard tree | 2 |
| 2025-01-01 | cycle trim | 6 |
| 2025-01-01 | hot spot | 5 |
| 2025-02-01 | hazard tree | 8 |
| 2025-02-01 | cycle trim | 5 |
| 2025-02-01 | hot spot | 4 |
| 2025-03-01 | hazard tree | 11 |
| 2025-03-01 | cycle trim | 16 |
| 2025-03-01 | hot spot | 3 |
| 2025-04-01 | hazard tree | 10 |
| 2025-04-01 | cycle trim | 16 |
| 2025-04-01 | hot spot | 10 |
| 2025-05-01 | hazard tree | 10 |
| 2025-05-01 | cycle trim | 8 |
| 2025-05-01 | hot spot | 7 |
| 2025-06-01 | hazard tree | 7 |
| 2025-06-01 | cycle trim | 4 |
| 2025-06-01 | hot spot | 7 |
| 2025-07-01 | hazard tree | 6 |
| 2025-07-01 | cycle trim | 5 |
| 2025-07-01 | hot spot | 9 |
| 2025-08-01 | hazard tree | 1 |
| 2025-08-01 | cycle trim | 5 |
| 2025-08-01 | hot spot | 3 |
| 2025-09-01 | hazard tree | 3 |
| 2025-09-01 | cycle trim | 6 |
| 2025-09-01 | hot spot | 3 |
| 2025-10-01 | hazard tree | 4 |
| 2025-10-01 | cycle trim | 6 |
| 2025-10-01 | hot spot | 6 |
| 2025-11-01 | hazard tree | 1 |
| 2025-11-01 | cycle trim | 2 |
| 2025-11-01 | hot spot | 6 |
| 2025-12-01 | hazard tree | 8 |
| 2025-12-01 | cycle trim | 9 |
| 2025-12-01 | hot spot | 6 |
| 2026-01-01 | hazard tree | 13 |
| 2026-01-01 | cycle trim | 4 |
| 2026-01-01 | hot spot | 12 |
| 2026-02-01 | hazard tree | 6 |
| 2026-02-01 | cycle trim | 7 |
| 2026-02-01 | hot spot | 10 |
| 2026-03-01 | hazard tree | 8 |
| 2026-03-01 | cycle trim | 5 |
| 2026-03-01 | hot spot | 7 |
| 2026-04-01 | hazard tree | 12 |
| 2026-04-01 | cycle trim | 15 |
| 2026-04-01 | hot spot | 11 |
| 2026-05-01 | hazard tree | 10 |
| 2026-05-01 | cycle trim | 5 |
| 2026-05-01 | hot spot | 10 |
| 2026-06-01 | hazard tree | 12 |
| 2026-06-01 | cycle trim | 5 |
| 2026-06-01 | hot spot | 8 |
| 2026-07-01 | hazard tree | 5 |
| 2026-07-01 | cycle trim | 8 |
| 2026-07-01 | hot spot | 8 |
| 2026-08-01 | hazard tree | 5 |
| 2026-08-01 | cycle trim | 4 |
| 2026-08-01 | hot spot | 4 |
| 2026-09-01 | hazard tree | 3 |
| 2026-09-01 | cycle trim | 5 |
| 2026-09-01 | hot spot | 5 |

### Figure 24. Poles over time (install_year) [72]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1,967 | 1,973 | 15 |
| 1,973 | 1,978 | 30 |
| 1,978 | 1,984 | 44 |
| 1,984 | 1,990 | 34 |
| 1,990 | 1,996 | 36 |
| 1,996 | 2,001 | 45 |
| 2,001 | 2,007 | 55 |
| 2,007 | 2,013 | 50 |
| 2,013 | 2,018 | 52 |
| 2,018 | 2,024 | 89 |

### Figure 25. Feeder reliability by year over time (year) [73]

| period | value |
| --- | --- |
| 2022-01-01 | 79 |
| 2022-02-01 | 0 |
| 2022-03-01 | 0 |
| 2022-04-01 | 0 |
| 2022-05-01 | 0 |
| 2022-06-01 | 0 |
| 2022-07-01 | 0 |
| 2022-08-01 | 0 |
| 2022-09-01 | 0 |
| 2022-10-01 | 0 |
| 2022-11-01 | 0 |
| 2022-12-01 | 0 |
| 2023-01-01 | 79 |
| 2023-02-01 | 0 |
| 2023-03-01 | 0 |
| 2023-04-01 | 0 |
| 2023-05-01 | 0 |
| 2023-06-01 | 0 |
| 2023-07-01 | 0 |
| 2023-08-01 | 0 |
| 2023-09-01 | 0 |
| 2023-10-01 | 0 |
| 2023-11-01 | 0 |
| 2023-12-01 | 0 |
| 2024-01-01 | 79 |
| 2024-02-01 | 0 |
| 2024-03-01 | 0 |
| 2024-04-01 | 0 |
| 2024-05-01 | 0 |
| 2024-06-01 | 0 |
| 2024-07-01 | 0 |
| 2024-08-01 | 0 |
| 2024-09-01 | 0 |
| 2024-10-01 | 0 |
| 2024-11-01 | 0 |
| 2024-12-01 | 0 |
| 2025-01-01 | 79 |

### Figure 26. Transformer assets over time (install_year) [74]

| bin_start | bin_end | count |
| --- | --- | --- |
| 25 | 39.2 | 66 |
| 39.2 | 53.4 | 59 |
| 53.4 | 67.6 | 0 |
| 67.6 | 81.8 | 61 |
| 81.8 | 96 | 0 |
| 96 | 110.2 | 48 |
| 110.2 | 124.4 | 0 |
| 124.4 | 138.6 | 0 |
| 138.6 | 152.8 | 0 |
| 152.8 | 167 | 66 |

### Figure 27. Emerging hot spots: Outages [75]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.044 | -0.2486 | 36 |
| -0.2486 | 0.5467 | 20 |
| 0.5467 | 1.342 | 21 |
| 1.342 | 2.138 | 10 |
| 2.138 | 2.933 | 4 |
| 2.933 | 3.728 | 5 |
| 3.728 | 4.524 | 3 |
| 4.524 | 5.319 | 1 |

### Figure 28. Emerging hot spots: Vegetation work orders [76]

| bin_start | bin_end | count |
| --- | --- | --- |
| -0.951 | -0.5264 | 39 |
| -0.5264 | -0.1017 | 28 |
| -0.1017 | 0.3229 | 1 |
| 0.3229 | 0.7475 | 9 |
| 0.7475 | 1.172 | 1 |
| 1.172 | 1.597 | 1 |
| 1.597 | 2.021 | 9 |
| 2.021 | 2.446 | 1 |

### Figure 29. Hot spot analysis: Poles [77]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 33 |
| -0.5 | 0 | not_significant | Not significant | 13 |
| 0 | 0.5 | not_significant | Not significant | 13 |
| 0.5 | 1 | not_significant | Not significant | 5 |
| 1 | 1.5 | not_significant | Not significant | 10 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 1 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 1 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 30. Hot spot analysis: Outages [78]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -2.5 | -2 | cold_95 | Cold spot, 95% confidence | 6 |
| -2 | -1.5 | cold_90 | Cold spot, 90% confidence | 45 |
| -2 | -1.5 | cold_95 | Cold spot, 95% confidence | 1 |
| -2 | -1.5 | not_significant | Not significant | 18 |
| -1.5 | -1 | not_significant | Not significant | 179 |
| -1 | -0.5 | not_significant | Not significant | 376 |
| -0.5 | 0 | not_significant | Not significant | 330 |
| 0 | 0.5 | not_significant | Not significant | 246 |
| 0.5 | 1 | not_significant | Not significant | 205 |
| 1 | 1.5 | not_significant | Not significant | 160 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 25 |
| 1.5 | 2 | hot_95 | Hot spot, 95% confidence | 3 |
| 1.5 | 2 | not_significant | Not significant | 15 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 28 |
| 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 3 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 7 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 2 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 31. Hot spot analysis: Vegetation work orders [79]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 33 |
| -0.5 | 0 | not_significant | Not significant | 17 |
| 0 | 0.5 | not_significant | Not significant | 13 |
| 0.5 | 1 | not_significant | Not significant | 10 |
| 1 | 1.5 | not_significant | Not significant | 5 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 2 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 2 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 1 |
| 4 | 4.5 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 32. Calculate density: Poles [80]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2.3 | 18 |
| 2.3 | 4.6 | 28 |
| 4.6 | 6.9 | 33 |
| 6.9 | 9.2 | 51 |
| 9.2 | 11.5 | 36 |
| 11.5 | 13.8 | 33 |
| 13.8 | 16.1 | 50 |
| 16.1 | 18.4 | 34 |
| 18.4 | 20.7 | 32 |
| 20.7 | 23 | 29 |

### Figure 33. Calculate density: Outages [81]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 4.5 | 37 |
| 4.5 | 9 | 59 |
| 9 | 13.5 | 160 |
| 13.5 | 18 | 118 |
| 18 | 22.5 | 189 |
| 22.5 | 27 | 166 |
| 27 | 31.5 | 179 |
| 31.5 | 36 | 129 |
| 36 | 40.5 | 190 |
| 40.5 | 45 | 144 |
| 45 | 49.5 | 137 |
| 49.5 | 54 | 80 |

### Figure 34. Calculate density: Vegetation work orders [82]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 2.3 | 18 |
| 2.3 | 4.6 | 27 |
| 4.6 | 6.9 | 34 |
| 6.9 | 9.2 | 53 |
| 9.2 | 11.5 | 38 |
| 11.5 | 13.8 | 32 |
| 13.8 | 16.1 | 52 |
| 16.1 | 18.4 | 32 |
| 18.4 | 20.7 | 32 |
| 20.7 | 23 | 32 |

### Figure 35. Summarize within: Service areas [83]

| bin_start | bin_end | count |
| --- | --- | --- |
| 19 | 24.6 | 3 |
| 24.6 | 30.2 | 4 |
| 30.2 | 35.8 | 3 |
| 35.8 | 41.4 | 1 |
| 41.4 | 47 | 3 |

### Figure 36. Summarize within: Service areas [84]

| bin_start | bin_end | count |
| --- | --- | --- |
| 57 | 85.8 | 4 |
| 85.8 | 114.6 | 5 |
| 114.6 | 143.4 | 0 |
| 143.4 | 172.2 | 2 |
| 172.2 | 201 | 3 |

### Figure 37. Summarize within: Service areas [85]

| bin_start | bin_end | count |
| --- | --- | --- |
| 18 | 26 | 7 |
| 26 | 34 | 1 |
| 34 | 42 | 1 |
| 42 | 50 | 1 |
| 50 | 58 | 4 |

### Figure 38. Find point clusters: Poles [86]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 149 | 0.3311 | 33.1% |
| 2 | 49 | 0.1089 | 10.9% |
| 5 | 35 | 0.07778 | 7.8% |
| 4 | 34 | 0.07556 | 7.6% |
| 6 | 31 | 0.06889 | 6.9% |
| 8 | 26 | 0.05778 | 5.8% |
| 3 | 23 | 0.05111 | 5.1% |
| 7 | 21 | 0.04667 | 4.7% |
| 9 | 19 | 0.04222 | 4.2% |
| 11 | 17 | 0.03778 | 3.8% |
| 0 | 16 | 0.03556 | 3.6% |
| 1 | 15 | 0.03333 | 3.3% |
| 10 | 15 | 0.03333 | 3.3% |

### Figure 39. Find point clusters: cluster hulls [87]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.08333 | 8.3% |
| 1 | 1 | 0.08333 | 8.3% |
| 10 | 1 | 0.08333 | 8.3% |
| 11 | 1 | 0.08333 | 8.3% |
| 2 | 1 | 0.08333 | 8.3% |
| 3 | 1 | 0.08333 | 8.3% |
| 4 | 1 | 0.08333 | 8.3% |
| 5 | 1 | 0.08333 | 8.3% |
| 6 | 1 | 0.08333 | 8.3% |
| 7 | 1 | 0.08333 | 8.3% |
| 8 | 1 | 0.08333 | 8.3% |
| 9 | 1 | 0.08333 | 8.3% |

### Figure 40. Find point clusters: Outages [88]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 985 | 0.597 | 59.7% |
| 5 | 121 | 0.07333 | 7.3% |
| 0 | 112 | 0.06788 | 6.8% |
| 1 | 81 | 0.04909 | 4.9% |
| 7 | 76 | 0.04606 | 4.6% |
| 3 | 56 | 0.03394 | 3.4% |
| 4 | 54 | 0.03273 | 3.3% |
| 6 | 34 | 0.02061 | 2.1% |
| 8 | 34 | 0.02061 | 2.1% |
| 2 | 32 | 0.01939 | 1.9% |
| 9 | 31 | 0.01879 | 1.9% |
| 10 | 20 | 0.01212 | 1.2% |
| 11 | 14 | 0.008485 | 0.8% |

### Figure 41. Find point clusters: cluster hulls [89]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.08333 | 8.3% |
| 1 | 1 | 0.08333 | 8.3% |
| 10 | 1 | 0.08333 | 8.3% |
| 11 | 1 | 0.08333 | 8.3% |
| 2 | 1 | 0.08333 | 8.3% |
| 3 | 1 | 0.08333 | 8.3% |
| 4 | 1 | 0.08333 | 8.3% |
| 5 | 1 | 0.08333 | 8.3% |
| 6 | 1 | 0.08333 | 8.3% |
| 7 | 1 | 0.08333 | 8.3% |
| 8 | 1 | 0.08333 | 8.3% |
| 9 | 1 | 0.08333 | 8.3% |

### Figure 42. Find point clusters: Vegetation work orders [90]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 157 | 0.3271 | 32.7% |
| 0 | 83 | 0.1729 | 17.3% |
| 1 | 56 | 0.1167 | 11.7% |
| 2 | 39 | 0.08125 | 8.1% |
| 4 | 36 | 0.075 | 7.5% |
| 7 | 36 | 0.075 | 7.5% |
| 6 | 20 | 0.04167 | 4.2% |
| 3 | 19 | 0.03958 | 4.0% |
| 5 | 19 | 0.03958 | 4.0% |
| 8 | 15 | 0.03125 | 3.1% |

### Figure 43. Find point clusters: cluster hulls [91]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.1111 | 11.1% |
| 1 | 1 | 0.1111 | 11.1% |
| 2 | 1 | 0.1111 | 11.1% |
| 3 | 1 | 0.1111 | 11.1% |
| 4 | 1 | 0.1111 | 11.1% |
| 5 | 1 | 0.1111 | 11.1% |
| 6 | 1 | 0.1111 | 11.1% |
| 7 | 1 | 0.1111 | 11.1% |
| 8 | 1 | 0.1111 | 11.1% |

### Figure 44. Find outliers: Service areas [92]

| value | lag | type |
| --- | --- | --- |
| -0.3267 | 2.336 | NS |
| 0.129 | 0.5255 | NS |
| -1.064 | -0.2187 | NS |
| -0.2187 | -0.8003 | NS |
| -0.4868 | -0.5612 | NS |
| 0.642 | -1.518 | NS |
| 1.464 | 0.129 | NS |
| 2.336 | -0.3267 | NS |
| -0.289 | -0.0449 | NS |
| 0.4018 | 0.129 | NS |
| -1.361 | -0.2016 | NS |
| -1.518 | 0.0776 | NS |
| 0.1915 | -0.4868 | NS |
| 0.1007 | -0.924 | NS |

### Figure 45. Repeat and near repeat classification: Outages [93]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | vegetation | 28 |
| 2024-10-01 | equipment | 31 |
| 2024-10-01 | weather | 12 |
| 2024-10-01 | animal | 10 |
| 2024-10-01 | unknown | 6 |
| 2024-10-01 | Other | 1 |
| 2024-11-01 | vegetation | 13 |
| 2024-11-01 | equipment | 15 |
| 2024-11-01 | weather | 5 |
| 2024-11-01 | animal | 3 |
| 2024-11-01 | unknown | 2 |
| 2024-11-01 | Other | 1 |
| 2024-12-01 | vegetation | 15 |
| 2024-12-01 | equipment | 10 |
| 2024-12-01 | weather | 8 |
| 2024-12-01 | animal | 2 |
| 2024-12-01 | unknown | 1 |
| 2024-12-01 | Other | 0 |
| 2025-01-01 | vegetation | 20 |
| 2025-01-01 | equipment | 19 |
| 2025-01-01 | weather | 7 |
| 2025-01-01 | animal | 4 |
| 2025-01-01 | unknown | 3 |
| 2025-01-01 | Other | 1 |
| 2025-02-01 | vegetation | 25 |
| 2025-02-01 | equipment | 15 |
| 2025-02-01 | weather | 9 |
| 2025-02-01 | animal | 2 |
| 2025-02-01 | unknown | 2 |
| 2025-02-01 | Other | 2 |
| 2025-03-01 | vegetation | 25 |
| 2025-03-01 | equipment | 8 |
| 2025-03-01 | weather | 3 |
| 2025-03-01 | animal | 3 |
| 2025-03-01 | unknown | 2 |
| 2025-03-01 | Other | 1 |
| 2025-04-01 | vegetation | 15 |
| 2025-04-01 | equipment | 6 |
| 2025-04-01 | weather | 6 |
| 2025-04-01 | animal | 7 |
| 2025-04-01 | unknown | 4 |
| 2025-04-01 | Other | 1 |
| 2025-05-01 | vegetation | 22 |
| 2025-05-01 | equipment | 9 |
| 2025-05-01 | weather | 12 |
| 2025-05-01 | animal | 3 |
| 2025-05-01 | unknown | 2 |
| 2025-05-01 | Other | 0 |
| 2025-06-01 | vegetation | 28 |
| 2025-06-01 | equipment | 24 |
| 2025-06-01 | weather | 7 |
| 2025-06-01 | animal | 6 |
| 2025-06-01 | unknown | 5 |
| 2025-06-01 | Other | 3 |
| 2025-07-01 | vegetation | 37 |
| 2025-07-01 | equipment | 20 |
| 2025-07-01 | weather | 9 |
| 2025-07-01 | animal | 3 |
| 2025-07-01 | unknown | 5 |
| 2025-07-01 | Other | 2 |
| 2025-08-01 | vegetation | 26 |
| 2025-08-01 | equipment | 30 |
| 2025-08-01 | weather | 143 |
| 2025-08-01 | animal | 4 |
| 2025-08-01 | unknown | 1 |
| 2025-08-01 | Other | 3 |
| 2025-09-01 | vegetation | 50 |
| 2025-09-01 | equipment | 26 |
| 2025-09-01 | weather | 13 |
| 2025-09-01 | animal | 6 |
| 2025-09-01 | unknown | 0 |
| 2025-09-01 | Other | 6 |
| 2025-10-01 | vegetation | 43 |
| 2025-10-01 | equipment | 28 |
| 2025-10-01 | weather | 16 |
| 2025-10-01 | animal | 8 |
| 2025-10-01 | unknown | 4 |
| 2025-10-01 | Other | 3 |
| 2025-11-01 | vegetation | 18 |
| 2025-11-01 | equipment | 14 |
| 2025-11-01 | weather | 7 |
| 2025-11-01 | animal | 2 |
| 2025-11-01 | unknown | 3 |
| 2025-11-01 | Other | 1 |
| 2025-12-01 | vegetation | 18 |
| 2025-12-01 | equipment | 11 |
| 2025-12-01 | weather | 6 |
| 2025-12-01 | animal | 3 |
| 2025-12-01 | unknown | 0 |
| 2025-12-01 | Other | 0 |
| 2026-01-01 | vegetation | 39 |
| 2026-01-01 | equipment | 15 |
| 2026-01-01 | weather | 6 |
| 2026-01-01 | animal | 8 |
| 2026-01-01 | unknown | 7 |
| 2026-01-01 | Other | 4 |
| 2026-02-01 | vegetation | 23 |
| 2026-02-01 | equipment | 12 |
| 2026-02-01 | weather | 6 |
| 2026-02-01 | animal | 3 |
| 2026-02-01 | unknown | 3 |
| 2026-02-01 | Other | 2 |
| 2026-03-01 | vegetation | 19 |
| 2026-03-01 | equipment | 9 |
| 2026-03-01 | weather | 5 |
| 2026-03-01 | animal | 4 |
| 2026-03-01 | unknown | 3 |
| 2026-03-01 | Other | 0 |
| 2026-04-01 | vegetation | 23 |
| 2026-04-01 | equipment | 12 |
| 2026-04-01 | weather | 6 |
| 2026-04-01 | animal | 4 |
| 2026-04-01 | unknown | 4 |
| 2026-04-01 | Other | 0 |
| 2026-05-01 | vegetation | 18 |
| 2026-05-01 | equipment | 5 |
| 2026-05-01 | weather | 3 |
| 2026-05-01 | animal | 5 |
| 2026-05-01 | unknown | 2 |
| 2026-05-01 | Other | 2 |
| 2026-06-01 | vegetation | 38 |
| 2026-06-01 | equipment | 32 |
| 2026-06-01 | weather | 12 |
| 2026-06-01 | animal | 5 |
| 2026-06-01 | unknown | 2 |
| 2026-06-01 | Other | 3 |
| 2026-07-01 | vegetation | 39 |
| 2026-07-01 | equipment | 27 |
| 2026-07-01 | weather | 13 |
| 2026-07-01 | animal | 7 |
| 2026-07-01 | unknown | 5 |
| 2026-07-01 | Other | 5 |
| 2026-08-01 | vegetation | 33 |
| 2026-08-01 | equipment | 24 |
| 2026-08-01 | weather | 5 |
| 2026-08-01 | animal | 10 |
| 2026-08-01 | unknown | 1 |
| 2026-08-01 | Other | 1 |
| 2026-09-01 | vegetation | 29 |
| 2026-09-01 | equipment | 25 |
| 2026-09-01 | weather | 24 |
| 2026-09-01 | animal | 7 |
| 2026-09-01 | unknown | 7 |
| 2026-09-01 | Other | 1 |

### Figure 46. Repeat and near repeat classification: Outages over time [94]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | vegetation | 28 |
| 2024-10-01 | equipment | 31 |
| 2024-10-01 | weather | 12 |
| 2024-10-01 | animal | 10 |
| 2024-10-01 | unknown | 6 |
| 2024-10-01 | Other | 1 |
| 2024-11-01 | vegetation | 13 |
| 2024-11-01 | equipment | 15 |
| 2024-11-01 | weather | 5 |
| 2024-11-01 | animal | 3 |
| 2024-11-01 | unknown | 2 |
| 2024-11-01 | Other | 1 |
| 2024-12-01 | vegetation | 15 |
| 2024-12-01 | equipment | 10 |
| 2024-12-01 | weather | 8 |
| 2024-12-01 | animal | 2 |
| 2024-12-01 | unknown | 1 |
| 2024-12-01 | Other | 0 |
| 2025-01-01 | vegetation | 20 |
| 2025-01-01 | equipment | 19 |
| 2025-01-01 | weather | 7 |
| 2025-01-01 | animal | 4 |
| 2025-01-01 | unknown | 3 |
| 2025-01-01 | Other | 1 |
| 2025-02-01 | vegetation | 25 |
| 2025-02-01 | equipment | 15 |
| 2025-02-01 | weather | 9 |
| 2025-02-01 | animal | 2 |
| 2025-02-01 | unknown | 2 |
| 2025-02-01 | Other | 2 |
| 2025-03-01 | vegetation | 25 |
| 2025-03-01 | equipment | 8 |
| 2025-03-01 | weather | 3 |
| 2025-03-01 | animal | 3 |
| 2025-03-01 | unknown | 2 |
| 2025-03-01 | Other | 1 |
| 2025-04-01 | vegetation | 15 |
| 2025-04-01 | equipment | 6 |
| 2025-04-01 | weather | 6 |
| 2025-04-01 | animal | 7 |
| 2025-04-01 | unknown | 4 |
| 2025-04-01 | Other | 1 |
| 2025-05-01 | vegetation | 22 |
| 2025-05-01 | equipment | 9 |
| 2025-05-01 | weather | 12 |
| 2025-05-01 | animal | 3 |
| 2025-05-01 | unknown | 2 |
| 2025-05-01 | Other | 0 |
| 2025-06-01 | vegetation | 28 |
| 2025-06-01 | equipment | 24 |
| 2025-06-01 | weather | 7 |
| 2025-06-01 | animal | 6 |
| 2025-06-01 | unknown | 5 |
| 2025-06-01 | Other | 3 |
| 2025-07-01 | vegetation | 37 |
| 2025-07-01 | equipment | 20 |
| 2025-07-01 | weather | 9 |
| 2025-07-01 | animal | 3 |
| 2025-07-01 | unknown | 5 |
| 2025-07-01 | Other | 2 |
| 2025-08-01 | vegetation | 26 |
| 2025-08-01 | equipment | 30 |
| 2025-08-01 | weather | 143 |
| 2025-08-01 | animal | 4 |
| 2025-08-01 | unknown | 1 |
| 2025-08-01 | Other | 3 |
| 2025-09-01 | vegetation | 50 |
| 2025-09-01 | equipment | 26 |
| 2025-09-01 | weather | 13 |
| 2025-09-01 | animal | 6 |
| 2025-09-01 | unknown | 0 |
| 2025-09-01 | Other | 6 |
| 2025-10-01 | vegetation | 43 |
| 2025-10-01 | equipment | 28 |
| 2025-10-01 | weather | 16 |
| 2025-10-01 | animal | 8 |
| 2025-10-01 | unknown | 4 |
| 2025-10-01 | Other | 3 |
| 2025-11-01 | vegetation | 18 |
| 2025-11-01 | equipment | 14 |
| 2025-11-01 | weather | 7 |
| 2025-11-01 | animal | 2 |
| 2025-11-01 | unknown | 3 |
| 2025-11-01 | Other | 1 |
| 2025-12-01 | vegetation | 18 |
| 2025-12-01 | equipment | 11 |
| 2025-12-01 | weather | 6 |
| 2025-12-01 | animal | 3 |
| 2025-12-01 | unknown | 0 |
| 2025-12-01 | Other | 0 |
| 2026-01-01 | vegetation | 39 |
| 2026-01-01 | equipment | 15 |
| 2026-01-01 | weather | 6 |
| 2026-01-01 | animal | 8 |
| 2026-01-01 | unknown | 7 |
| 2026-01-01 | Other | 4 |
| 2026-02-01 | vegetation | 23 |
| 2026-02-01 | equipment | 12 |
| 2026-02-01 | weather | 6 |
| 2026-02-01 | animal | 3 |
| 2026-02-01 | unknown | 3 |
| 2026-02-01 | Other | 2 |
| 2026-03-01 | vegetation | 19 |
| 2026-03-01 | equipment | 9 |
| 2026-03-01 | weather | 5 |
| 2026-03-01 | animal | 4 |
| 2026-03-01 | unknown | 3 |
| 2026-03-01 | Other | 0 |
| 2026-04-01 | vegetation | 23 |
| 2026-04-01 | equipment | 12 |
| 2026-04-01 | weather | 6 |
| 2026-04-01 | animal | 4 |
| 2026-04-01 | unknown | 4 |
| 2026-04-01 | Other | 0 |
| 2026-05-01 | vegetation | 18 |
| 2026-05-01 | equipment | 5 |
| 2026-05-01 | weather | 3 |
| 2026-05-01 | animal | 5 |
| 2026-05-01 | unknown | 2 |
| 2026-05-01 | Other | 2 |
| 2026-06-01 | vegetation | 38 |
| 2026-06-01 | equipment | 32 |
| 2026-06-01 | weather | 12 |
| 2026-06-01 | animal | 5 |
| 2026-06-01 | unknown | 2 |
| 2026-06-01 | Other | 3 |
| 2026-07-01 | vegetation | 39 |
| 2026-07-01 | equipment | 27 |
| 2026-07-01 | weather | 13 |
| 2026-07-01 | animal | 7 |
| 2026-07-01 | unknown | 5 |
| 2026-07-01 | Other | 5 |
| 2026-08-01 | vegetation | 33 |
| 2026-08-01 | equipment | 24 |
| 2026-08-01 | weather | 5 |
| 2026-08-01 | animal | 10 |
| 2026-08-01 | unknown | 1 |
| 2026-08-01 | Other | 1 |
| 2026-09-01 | vegetation | 29 |
| 2026-09-01 | equipment | 25 |
| 2026-09-01 | weather | 24 |
| 2026-09-01 | animal | 7 |
| 2026-09-01 | unknown | 7 |
| 2026-09-01 | Other | 1 |

### Figure 47. Find point clusters: Vegetation work orders over time [95]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | 11 | 2 |
| 2024-10-01 | 6 | 0 |
| 2024-10-01 | 9 | 2 |
| 2024-10-01 | 12 | 0 |
| 2024-10-01 | 4 | 1 |
| 2024-10-01 | Other | 6 |
| 2024-11-01 | 11 | 0 |
| 2024-11-01 | 6 | 3 |
| 2024-11-01 | 9 | 0 |
| 2024-11-01 | 12 | 1 |
| 2024-11-01 | 4 | 1 |
| 2024-11-01 | Other | 7 |
| 2024-12-01 | 11 | 5 |
| 2024-12-01 | 6 | 0 |
| 2024-12-01 | 9 | 1 |
| 2024-12-01 | 12 | 3 |
| 2024-12-01 | 4 | 3 |
| 2024-12-01 | Other | 10 |
| 2025-01-01 | 11 | 2 |
| 2025-01-01 | 6 | 3 |
| 2025-01-01 | 9 | 0 |
| 2025-01-01 | 12 | 0 |
| 2025-01-01 | 4 | 3 |
| 2025-01-01 | Other | 5 |
| 2025-02-01 | 11 | 1 |
| 2025-02-01 | 6 | 2 |
| 2025-02-01 | 9 | 1 |
| 2025-02-01 | 12 | 2 |
| 2025-02-01 | 4 | 1 |
| 2025-02-01 | Other | 10 |
| 2025-03-01 | 11 | 1 |
| 2025-03-01 | 6 | 3 |
| 2025-03-01 | 9 | 5 |
| 2025-03-01 | 12 | 3 |
| 2025-03-01 | 4 | 2 |
| 2025-03-01 | Other | 16 |
| 2025-04-01 | 11 | 4 |
| 2025-04-01 | 6 | 3 |
| 2025-04-01 | 9 | 3 |
| 2025-04-01 | 12 | 3 |
| 2025-04-01 | 4 | 6 |
| 2025-04-01 | Other | 17 |
| 2025-05-01 | 11 | 2 |
| 2025-05-01 | 6 | 0 |
| 2025-05-01 | 9 | 2 |
| 2025-05-01 | 12 | 3 |
| 2025-05-01 | 4 | 2 |
| 2025-05-01 | Other | 16 |
| 2025-06-01 | 11 | 1 |
| 2025-06-01 | 6 | 1 |
| 2025-06-01 | 9 | 3 |
| 2025-06-01 | 12 | 1 |
| 2025-06-01 | 4 | 1 |
| 2025-06-01 | Other | 11 |
| 2025-07-01 | 11 | 2 |
| 2025-07-01 | 6 | 4 |
| 2025-07-01 | 9 | 4 |
| 2025-07-01 | 12 | 1 |
| 2025-07-01 | 4 | 2 |
| 2025-07-01 | Other | 7 |
| 2025-08-01 | 11 | 1 |
| 2025-08-01 | 6 | 0 |
| 2025-08-01 | 9 | 1 |
| 2025-08-01 | 12 | 0 |
| 2025-08-01 | 4 | 0 |
| 2025-08-01 | Other | 7 |
| 2025-09-01 | 11 | 1 |
| 2025-09-01 | 6 | 1 |
| 2025-09-01 | 9 | 2 |
| 2025-09-01 | 12 | 1 |
| 2025-09-01 | 4 | 2 |
| 2025-09-01 | Other | 5 |
| 2025-10-01 | 11 | 1 |
| 2025-10-01 | 6 | 3 |
| 2025-10-01 | 9 | 0 |
| 2025-10-01 | 12 | 2 |
| 2025-10-01 | 4 | 1 |
| 2025-10-01 | Other | 9 |
| 2025-11-01 | 11 | 1 |
| 2025-11-01 | 6 | 0 |
| 2025-11-01 | 9 | 1 |
| 2025-11-01 | 12 | 2 |
| 2025-11-01 | 4 | 2 |
| 2025-11-01 | Other | 3 |
| 2025-12-01 | 11 | 1 |
| 2025-12-01 | 6 | 3 |
| 2025-12-01 | 9 | 1 |
| 2025-12-01 | 12 | 3 |
| 2025-12-01 | 4 | 1 |
| 2025-12-01 | Other | 14 |
| 2026-01-01 | 11 | 3 |
| 2026-01-01 | 6 | 6 |
| 2026-01-01 | 9 | 1 |
| 2026-01-01 | 12 | 3 |
| 2026-01-01 | 4 | 2 |
| 2026-01-01 | Other | 14 |
| 2026-02-01 | 11 | 3 |
| 2026-02-01 | 6 | 2 |
| 2026-02-01 | 9 | 3 |
| 2026-02-01 | 12 | 3 |
| 2026-02-01 | 4 | 3 |
| 2026-02-01 | Other | 9 |
| 2026-03-01 | 11 | 1 |
| 2026-03-01 | 6 | 0 |
| 2026-03-01 | 9 | 2 |
| 2026-03-01 | 12 | 1 |
| 2026-03-01 | 4 | 4 |
| 2026-03-01 | Other | 12 |
| 2026-04-01 | 11 | 2 |
| 2026-04-01 | 6 | 4 |
| 2026-04-01 | 9 | 5 |
| 2026-04-01 | 12 | 3 |
| 2026-04-01 | 4 | 2 |
| 2026-04-01 | Other | 22 |
| 2026-05-01 | 11 | 3 |
| 2026-05-01 | 6 | 3 |
| 2026-05-01 | 9 | 1 |
| 2026-05-01 | 12 | 0 |
| 2026-05-01 | 4 | 1 |
| 2026-05-01 | Other | 17 |
| 2026-06-01 | 11 | 3 |
| 2026-06-01 | 6 | 1 |
| 2026-06-01 | 9 | 3 |
| 2026-06-01 | 12 | 2 |
| 2026-06-01 | 4 | 1 |
| 2026-06-01 | Other | 15 |
| 2026-07-01 | 11 | 2 |
| 2026-07-01 | 6 | 0 |
| 2026-07-01 | 9 | 0 |
| 2026-07-01 | 12 | 2 |
| 2026-07-01 | 4 | 1 |
| 2026-07-01 | Other | 16 |
| 2026-08-01 | 11 | 1 |
| 2026-08-01 | 6 | 2 |
| 2026-08-01 | 9 | 2 |
| 2026-08-01 | 12 | 0 |
| 2026-08-01 | 4 | 0 |
| 2026-08-01 | Other | 8 |
| 2026-09-01 | 11 | 3 |
| 2026-09-01 | 6 | 1 |
| 2026-09-01 | 9 | 0 |
| 2026-09-01 | 12 | 3 |
| 2026-09-01 | 4 | 0 |
| 2026-09-01 | Other | 6 |

### Figure 48. Find point clusters: Outages over time [96]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | vegetation | 28 |
| 2024-10-01 | equipment | 31 |
| 2024-10-01 | weather | 12 |
| 2024-10-01 | animal | 10 |
| 2024-10-01 | unknown | 6 |
| 2024-10-01 | Other | 1 |
| 2024-11-01 | vegetation | 13 |
| 2024-11-01 | equipment | 15 |
| 2024-11-01 | weather | 5 |
| 2024-11-01 | animal | 3 |
| 2024-11-01 | unknown | 2 |
| 2024-11-01 | Other | 1 |
| 2024-12-01 | vegetation | 15 |
| 2024-12-01 | equipment | 10 |
| 2024-12-01 | weather | 8 |
| 2024-12-01 | animal | 2 |
| 2024-12-01 | unknown | 1 |
| 2024-12-01 | Other | 0 |
| 2025-01-01 | vegetation | 20 |
| 2025-01-01 | equipment | 19 |
| 2025-01-01 | weather | 7 |
| 2025-01-01 | animal | 4 |
| 2025-01-01 | unknown | 3 |
| 2025-01-01 | Other | 1 |
| 2025-02-01 | vegetation | 25 |
| 2025-02-01 | equipment | 15 |
| 2025-02-01 | weather | 9 |
| 2025-02-01 | animal | 2 |
| 2025-02-01 | unknown | 2 |
| 2025-02-01 | Other | 2 |
| 2025-03-01 | vegetation | 25 |
| 2025-03-01 | equipment | 8 |
| 2025-03-01 | weather | 3 |
| 2025-03-01 | animal | 3 |
| 2025-03-01 | unknown | 2 |
| 2025-03-01 | Other | 1 |
| 2025-04-01 | vegetation | 15 |
| 2025-04-01 | equipment | 6 |
| 2025-04-01 | weather | 6 |
| 2025-04-01 | animal | 7 |
| 2025-04-01 | unknown | 4 |
| 2025-04-01 | Other | 1 |
| 2025-05-01 | vegetation | 22 |
| 2025-05-01 | equipment | 9 |
| 2025-05-01 | weather | 12 |
| 2025-05-01 | animal | 3 |
| 2025-05-01 | unknown | 2 |
| 2025-05-01 | Other | 0 |
| 2025-06-01 | vegetation | 28 |
| 2025-06-01 | equipment | 24 |
| 2025-06-01 | weather | 7 |
| 2025-06-01 | animal | 6 |
| 2025-06-01 | unknown | 5 |
| 2025-06-01 | Other | 3 |
| 2025-07-01 | vegetation | 37 |
| 2025-07-01 | equipment | 20 |
| 2025-07-01 | weather | 9 |
| 2025-07-01 | animal | 3 |
| 2025-07-01 | unknown | 5 |
| 2025-07-01 | Other | 2 |
| 2025-08-01 | vegetation | 26 |
| 2025-08-01 | equipment | 30 |
| 2025-08-01 | weather | 143 |
| 2025-08-01 | animal | 4 |
| 2025-08-01 | unknown | 1 |
| 2025-08-01 | Other | 3 |
| 2025-09-01 | vegetation | 50 |
| 2025-09-01 | equipment | 26 |
| 2025-09-01 | weather | 13 |
| 2025-09-01 | animal | 6 |
| 2025-09-01 | unknown | 0 |
| 2025-09-01 | Other | 6 |
| 2025-10-01 | vegetation | 43 |
| 2025-10-01 | equipment | 28 |
| 2025-10-01 | weather | 16 |
| 2025-10-01 | animal | 8 |
| 2025-10-01 | unknown | 4 |
| 2025-10-01 | Other | 3 |
| 2025-11-01 | vegetation | 18 |
| 2025-11-01 | equipment | 14 |
| 2025-11-01 | weather | 7 |
| 2025-11-01 | animal | 2 |
| 2025-11-01 | unknown | 3 |
| 2025-11-01 | Other | 1 |
| 2025-12-01 | vegetation | 18 |
| 2025-12-01 | equipment | 11 |
| 2025-12-01 | weather | 6 |
| 2025-12-01 | animal | 3 |
| 2025-12-01 | unknown | 0 |
| 2025-12-01 | Other | 0 |
| 2026-01-01 | vegetation | 39 |
| 2026-01-01 | equipment | 15 |
| 2026-01-01 | weather | 6 |
| 2026-01-01 | animal | 8 |
| 2026-01-01 | unknown | 7 |
| 2026-01-01 | Other | 4 |
| 2026-02-01 | vegetation | 23 |
| 2026-02-01 | equipment | 12 |
| 2026-02-01 | weather | 6 |
| 2026-02-01 | animal | 3 |
| 2026-02-01 | unknown | 3 |
| 2026-02-01 | Other | 2 |
| 2026-03-01 | vegetation | 19 |
| 2026-03-01 | equipment | 9 |
| 2026-03-01 | weather | 5 |
| 2026-03-01 | animal | 4 |
| 2026-03-01 | unknown | 3 |
| 2026-03-01 | Other | 0 |
| 2026-04-01 | vegetation | 23 |
| 2026-04-01 | equipment | 12 |
| 2026-04-01 | weather | 6 |
| 2026-04-01 | animal | 4 |
| 2026-04-01 | unknown | 4 |
| 2026-04-01 | Other | 0 |
| 2026-05-01 | vegetation | 18 |
| 2026-05-01 | equipment | 5 |
| 2026-05-01 | weather | 3 |
| 2026-05-01 | animal | 5 |
| 2026-05-01 | unknown | 2 |
| 2026-05-01 | Other | 2 |
| 2026-06-01 | vegetation | 38 |
| 2026-06-01 | equipment | 32 |
| 2026-06-01 | weather | 12 |
| 2026-06-01 | animal | 5 |
| 2026-06-01 | unknown | 2 |
| 2026-06-01 | Other | 3 |
| 2026-07-01 | vegetation | 39 |
| 2026-07-01 | equipment | 27 |
| 2026-07-01 | weather | 13 |
| 2026-07-01 | animal | 7 |
| 2026-07-01 | unknown | 5 |
| 2026-07-01 | Other | 5 |
| 2026-08-01 | vegetation | 33 |
| 2026-08-01 | equipment | 24 |
| 2026-08-01 | weather | 5 |
| 2026-08-01 | animal | 10 |
| 2026-08-01 | unknown | 1 |
| 2026-08-01 | Other | 1 |
| 2026-09-01 | vegetation | 29 |
| 2026-09-01 | equipment | 25 |
| 2026-09-01 | weather | 24 |
| 2026-09-01 | animal | 7 |
| 2026-09-01 | unknown | 7 |
| 2026-09-01 | Other | 1 |

### Figure 49. Hot spot analysis: Outages over time [97]

| period | series | value |
| --- | --- | --- |
| 2024-10-01 | vegetation | 28 |
| 2024-10-01 | equipment | 31 |
| 2024-10-01 | weather | 12 |
| 2024-10-01 | animal | 10 |
| 2024-10-01 | unknown | 6 |
| 2024-10-01 | Other | 1 |
| 2024-11-01 | vegetation | 13 |
| 2024-11-01 | equipment | 15 |
| 2024-11-01 | weather | 5 |
| 2024-11-01 | animal | 3 |
| 2024-11-01 | unknown | 2 |
| 2024-11-01 | Other | 1 |
| 2024-12-01 | vegetation | 15 |
| 2024-12-01 | equipment | 10 |
| 2024-12-01 | weather | 8 |
| 2024-12-01 | animal | 2 |
| 2024-12-01 | unknown | 1 |
| 2024-12-01 | Other | 0 |
| 2025-01-01 | vegetation | 20 |
| 2025-01-01 | equipment | 19 |
| 2025-01-01 | weather | 7 |
| 2025-01-01 | animal | 4 |
| 2025-01-01 | unknown | 3 |
| 2025-01-01 | Other | 1 |
| 2025-02-01 | vegetation | 25 |
| 2025-02-01 | equipment | 15 |
| 2025-02-01 | weather | 9 |
| 2025-02-01 | animal | 2 |
| 2025-02-01 | unknown | 2 |
| 2025-02-01 | Other | 2 |
| 2025-03-01 | vegetation | 25 |
| 2025-03-01 | equipment | 8 |
| 2025-03-01 | weather | 3 |
| 2025-03-01 | animal | 3 |
| 2025-03-01 | unknown | 2 |
| 2025-03-01 | Other | 1 |
| 2025-04-01 | vegetation | 15 |
| 2025-04-01 | equipment | 6 |
| 2025-04-01 | weather | 6 |
| 2025-04-01 | animal | 7 |
| 2025-04-01 | unknown | 4 |
| 2025-04-01 | Other | 1 |
| 2025-05-01 | vegetation | 22 |
| 2025-05-01 | equipment | 9 |
| 2025-05-01 | weather | 12 |
| 2025-05-01 | animal | 3 |
| 2025-05-01 | unknown | 2 |
| 2025-05-01 | Other | 0 |
| 2025-06-01 | vegetation | 28 |
| 2025-06-01 | equipment | 24 |
| 2025-06-01 | weather | 7 |
| 2025-06-01 | animal | 6 |
| 2025-06-01 | unknown | 5 |
| 2025-06-01 | Other | 3 |
| 2025-07-01 | vegetation | 37 |
| 2025-07-01 | equipment | 20 |
| 2025-07-01 | weather | 9 |
| 2025-07-01 | animal | 3 |
| 2025-07-01 | unknown | 5 |
| 2025-07-01 | Other | 2 |
| 2025-08-01 | vegetation | 26 |
| 2025-08-01 | equipment | 30 |
| 2025-08-01 | weather | 143 |
| 2025-08-01 | animal | 4 |
| 2025-08-01 | unknown | 1 |
| 2025-08-01 | Other | 3 |
| 2025-09-01 | vegetation | 50 |
| 2025-09-01 | equipment | 26 |
| 2025-09-01 | weather | 13 |
| 2025-09-01 | animal | 6 |
| 2025-09-01 | unknown | 0 |
| 2025-09-01 | Other | 6 |
| 2025-10-01 | vegetation | 43 |
| 2025-10-01 | equipment | 28 |
| 2025-10-01 | weather | 16 |
| 2025-10-01 | animal | 8 |
| 2025-10-01 | unknown | 4 |
| 2025-10-01 | Other | 3 |
| 2025-11-01 | vegetation | 18 |
| 2025-11-01 | equipment | 14 |
| 2025-11-01 | weather | 7 |
| 2025-11-01 | animal | 2 |
| 2025-11-01 | unknown | 3 |
| 2025-11-01 | Other | 1 |
| 2025-12-01 | vegetation | 18 |
| 2025-12-01 | equipment | 11 |
| 2025-12-01 | weather | 6 |
| 2025-12-01 | animal | 3 |
| 2025-12-01 | unknown | 0 |
| 2025-12-01 | Other | 0 |
| 2026-01-01 | vegetation | 39 |
| 2026-01-01 | equipment | 15 |
| 2026-01-01 | weather | 6 |
| 2026-01-01 | animal | 8 |
| 2026-01-01 | unknown | 7 |
| 2026-01-01 | Other | 4 |
| 2026-02-01 | vegetation | 23 |
| 2026-02-01 | equipment | 12 |
| 2026-02-01 | weather | 6 |
| 2026-02-01 | animal | 3 |
| 2026-02-01 | unknown | 3 |
| 2026-02-01 | Other | 2 |
| 2026-03-01 | vegetation | 19 |
| 2026-03-01 | equipment | 9 |
| 2026-03-01 | weather | 5 |
| 2026-03-01 | animal | 4 |
| 2026-03-01 | unknown | 3 |
| 2026-03-01 | Other | 0 |
| 2026-04-01 | vegetation | 23 |
| 2026-04-01 | equipment | 12 |
| 2026-04-01 | weather | 6 |
| 2026-04-01 | animal | 4 |
| 2026-04-01 | unknown | 4 |
| 2026-04-01 | Other | 0 |
| 2026-05-01 | vegetation | 18 |
| 2026-05-01 | equipment | 5 |
| 2026-05-01 | weather | 3 |
| 2026-05-01 | animal | 5 |
| 2026-05-01 | unknown | 2 |
| 2026-05-01 | Other | 2 |
| 2026-06-01 | vegetation | 38 |
| 2026-06-01 | equipment | 32 |
| 2026-06-01 | weather | 12 |
| 2026-06-01 | animal | 5 |
| 2026-06-01 | unknown | 2 |
| 2026-06-01 | Other | 3 |
| 2026-07-01 | vegetation | 39 |
| 2026-07-01 | equipment | 27 |
| 2026-07-01 | weather | 13 |
| 2026-07-01 | animal | 7 |
| 2026-07-01 | unknown | 5 |
| 2026-07-01 | Other | 5 |
| 2026-08-01 | vegetation | 33 |
| 2026-08-01 | equipment | 24 |
| 2026-08-01 | weather | 5 |
| 2026-08-01 | animal | 10 |
| 2026-08-01 | unknown | 1 |
| 2026-08-01 | Other | 1 |
| 2026-09-01 | vegetation | 29 |
| 2026-09-01 | equipment | 25 |
| 2026-09-01 | weather | 24 |
| 2026-09-01 | animal | 7 |
| 2026-09-01 | unknown | 7 |
| 2026-09-01 | Other | 1 |


*GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*