blocksnet.method.land_use_prediction

Classes

LandUsePrediction(*, city_model[, verbose])

A class used to predict land use based on various methods including cosine similarity.

class blocksnet.method.land_use_prediction.LandUsePrediction(*, city_model: City, verbose: bool = True)

Bases: BaseMethod

A class used to predict land use based on various methods including cosine similarity.

plot(gdf: gpd.GeoDataFrame, linewidth=0.1, figsize=(10, 10)):

Plots the GeoDataFrame with predicted land use.

_get_land_uses_services():

Retrieves land use service types for the city model.

_get_blocks_gdf():

Retrieves block geometries and their service capacities as a GeoDataFrame.

_get_unique_per_landuse(landuse_items):

Finds unique service tags for each land use.

_intersects(set1, set2):

Checks if two sets have any common elements.

_predict_block_landuse_cosine_similarity(block_vector, landuse_vectors, return_prob=False):

Predicts land use for a block using cosine similarity.

_predict_block_landuse(codes_in_block, landuse_items, use_cos_similarity=True):

Predicts the land use for a block.

calculate(use_cos_similarity=True):

Calculates the land use prediction for all blocks.

static plot(gdf: GeoDataFrame, linewidth: float = 0.1, figsize: tuple[int, int] = (10, 10))

Plots the GeoDataFrame with predicted land use.

Parameters:

• gdf (gpd.GeoDataFrame) – GeoDataFrame containing the geometries and predicted land use.

• linewidth (float, optional) – Size of the polygon border to plot, by default 0.1.

• figsize (tuple, optional) – Size of the figure to plot, by default (10, 10).

_get_land_uses_services() → dict[str, list[str]]

Retrieves land use service types for the city model.

Returns:

Dictionary with land use names as keys and lists of service types as values.

Return type:

dict

_get_blocks_gdf() → GeoDataFrame

Retrieves block geometries and their service capacities as a GeoDataFrame.

Returns:

GeoDataFrame with block geometries and boolean values representing the presence of each service type.

Return type:

gpd.GeoDataFrame

static _get_unique_per_landuse(landuse_items) → dict[blocksnet.models.land_use.LandUse, set[str]]

Finds unique service tags for each land use.

Parameters:

landuse_items (dict) – Dictionary containing service codes or service tags for each land use.

Returns:

Dictionary with land use categories as keys and sets of unique service tags as values.

Return type:

dict

static _intersects(set1, set2) → bool

Checks if two sets have any common elements.

Parameters:

• set1 (np.array | list | set) – First collection of elements.

• set2 (np.array | list | set) – Second collection of elements.

Returns:

True if there are common elements, False otherwise.

Return type:

bool

static _predict_block_landuse_cosine_similarity(block_vector, landuse_vectors, return_prob=False) → str | None

Predicts land use for a block using cosine similarity.

Parameters:

• block_vector (list | np.array) – Collection of booleans representing services present in a block.

• landuse_vectors (dict) – Dictionary containing vectors representing services present in each land use.

• return_prob (bool, optional) – If True, also returns the probability of the prediction, by default False.

Returns:

Predicted land use category or None if no prediction is made.

Return type:

str or None

_predict_block_landuse(codes_in_block, landuse_items, use_cos_similarity=True)

Predicts the land use for a block.

Parameters:

• codes_in_block (list | np.array) – List of service tags or service codes present in a block.

• landuse_items (dict) – Dictionary containing service codes or service tags for each land use.

• use_cos_similarity (bool, optional) – Use cosine similarity to predict unpredicted land uses for blocks, by default True.

Returns:

Predicted land use category or None if no prediction is made.

Return type:

str or None

calculate(use_cos_similarity=True)

Calculates the land use prediction for all blocks.

Parameters:

use_cos_similarity (bool, optional) – Use cosine similarity to predict unpredicted land uses for blocks, by default True.

Returns:

GeoDataFrame containing the geometries and predicted land use for all blocks.

Return type:

gpd.GeoDataFrame

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.