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+# Activity-based Model
+
+Guide to background and modelling assumptions made.
+
+## Assumptions
+
+### Geographies
+Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries will be used.
+
+#### Locations
+- Homes: OA11CD centroids
+- Work: exact location data
+- Education: exact location, travel time for a given mode is used to determine the feasible zone.
+- Shop (food):
+- Shop (other):
+- Visit:
+
+### Adding activity patterns to synthetic population
+
+#### NTS data 
+- We are currently using the entire NTS sample, but this could include trips with unrepresentative distances (e.g. commuting distance in London is not the same as liverpool). See https://github.com/Urban-Analytics-Technology-Platform/acbm/issues/16
+
+#### Household level matching 
+- We use categorical matching at the household level (level 1) and then propensity score matching (PSM) at the individual level (level 2)
+- We need to implement PSM from the beginning to ensure that each individual in the SPC is matched to at least one sample from the NTS. See https://github.com/Urban-Analytics-Technology-Platform/acbm/issues/13
+- Matching variables are decided using trial and error (see [2_match_households_and_individuals](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/d2f9e747c3d55148316661b13b1650fac4a5a4ad/notebooks/2_match_households_and_individuals.ipynb). Using PSM would allow us to use all variables
+- For each SPC household, we randomly select one of the matched NTS households
+- Rest of the assumptions are outlined in the [wiki page](https://github.com/Urban-Analytics-Technology-Platform/acbm/wiki/Adding-activity-patterns-to-synthetic-population)
+
+#### Individual level matching 
+- Done based on age_group and sex only. PSM without replacement
+  
+### Assigning activities to geographic locations
+
+#### Mode and trip purpose mapping 
+
+- The NTS has a detailed breakdown of modes. We create dictionaries to map these modes to more generic modes (less categories)
+- The same is done for trip purposes
+- You can see the mode_mapping and purp_mapping dictionaries in [3_locations_primary](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/3d1dc9bbd4651daa03c10c4e6140fc54cbdcc049/notebooks/3_locations_primary.ipynb)
+
+#### Home locations 
+
+- These are still MSOA centroids (as taken from SPC). These locations need to be edited so households are assigned to different houses
+
+#### Activity locations (education)
+
+##### Downloading and labeling POIs
+
+- We download osm locations and label them. This is currently done through osmox with a custom config json file. See [this example](https://github.com/arup-group/osmox/blob/main/configs/config_UK.json)
+- In the config file, we specify what to download from osm, and in "activity_mapping", we decide how each POI is labelled.
+- For education POIs, I've done the following:   
+
+> "kindergarden": ["education_kg", "work"],
+> 
+> "school": ["education_school", "work"],
+> 
+> "university": ["education_university", "work"],
+> 
+> "college": ["education_college", "work"],
+
+##### Selecting feasible zones for each activity
+
+- This is done in [get_possible_zones](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L201). The function looks at the travel mode and trip duration (from NTS), and uses a pre-calculated travel time matrix by mode to identify which zones can be reached for that mode and duration. It then selects activities that have a POI that matches our activity purpose `dact`
+- The function can return an empty list for some activities, as no zones match our constraints. We need to add logic to ensure at least one zone with a relevant POI is chosen. We can choose a POI based on distance and disregard the travel time matrix
+
+##### Selecting a zone from feasible zones
+
+- If an individual in the NTS has an "education" activity, I map their age to an education type. See the age_group_mapping dictionary in 3_locations_primary:
+
+> age_group_mapping = {
+> 
+> 1: "education_kg",   # "0-4"
+> 
+> 2: "education_school", # "5-10"
+> 
+> 3: "education_school", # "11-16"
+> 
+> 4: "education_university", # "17-20"
+> 
+> 5: "education_university", # "21-29"
+> 
+> 6: "education_university", # "30-39"
+> 
+> 7: "education_university", # "40-49"
+> 
+> 8: "education_university", # "50-59"
+> 
+> 9: "education_university" # "60+"
+> }
+
+    
+- When selecting a location for an education activity in [select_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L578), we try to select a zone that has a POI that matches the persons age group. If we can't we choose any other feasible zone with an education POI
+- This logic should be moved upstream to the [get_possible_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L201). For each activity, we should always ensure that our list of feasible zones has a zone with our specific POI category. This should be added in the [filter_by_activity](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L374) logic. The filter_by_activity logic currently looks at activity purpose from the NTS (e.g. "education"). We need to add the extra level of detail from age_group_mapping, and then filter based on that instead
+- We select a zone from the feasible zones probabilistically based on total floor area of the POIs that match the relevant activity. See [select_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L578)
+
+##### Selecting a POI
+- We choose a POI from the relevant POIs in the chosen zone.This is done probabilistically based on floor_area
+- Should this step be embedded in `select_zone()`
+
+
+#### TODO
+- edit `osmox` config to replace education_college with education_university. We should have mutually exclusive groups only and these two options serve the same age group
+- (**DONE** [here](https://github.com/Urban-Analytics-Technology-Platform/acbm/commit/6acecb928ea2b9bf26952eb45b86f2918a6dccdf)) migrate logic for age_group_mapping from `select_zone()` to `get_possible_zones()`
+- edit `get_possible_zones()` to ensure it never returns an empty list of zones. See above for how to do this
+    - 14/05/2024: I created another function `fill_missing_zones()`. see [this commit](https://github.com/Urban-Analytics-Technology-Platform/acbm/commit/10ae82b3923cdc51474d3721df80e332ea74ba03#diff-48d91584494e303c162dd8c5b8881de35f33976f2f688cd5a56db01b7ff1f233)
+
diff --git a/scripts/2.1_sandbox-match_households.py b/scripts/2.1_sandbox-match_households.py
index f11cf1a..cc53238 100644
--- a/scripts/2.1_sandbox-match_households.py
+++ b/scripts/2.1_sandbox-match_households.py
@@ -17,7 +17,7 @@
 
 # Seed RNG
 SEED = 0
-np.ranmdom.seed(SEED)
+np.random.seed(SEED)
 
 # ## Step 1: Load in the datasets