# libpysal.weights.DistanceBand

class libpysal.weights.DistanceBand(data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric='euclidean', radius=None)

  Spatial weights based on distance band.

  Parameters:
    data `array`

      (n,k) or KDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects

    threshold `python:float`

      distance band

    p `python:float`

      DEPRECATED: use distance_metric Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance

    binary bool

      If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0 If false wij=dij^{alpha}

    alpha `python:float`

      distance decay parameter for weight (default -1.0) if alpha is positive the weights will not decline with distance. If binary is True, alpha is ignored

    ids `python:list`

      values to use for keys of the neighbors and weights dicts

    build_sp bool

      DEPRECATED True to build sparse distance matrix and false to build dense distance matrix; significant speed gains may be obtained dending on the sparsity of the of distance_matrix and threshold that is applied

    silent bool

      By default libpysal will print a warning if the dataset contains any disconnected observations or islands. To silence this warning set this parameter to True.

  Attributes:
    weights `python:dict`

      of neighbor weights keyed by observation id

    neighbors `python:dict`

      of neighbors keyed by observation id

  Examples

      >>> import libpysal
      >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
      >>> wcheck = libpysal.weights.W(
      ...     {0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}
      ... )

  WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w=libpysal.weights.DistanceBand(points,threshold=11.2)

  WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> libpysal.weights.util.neighbor_equality(w, wcheck) True >>> w=libpysal.weights.DistanceBand(points,threshold=14.2) >>> wcheck = libpysal.weights.W( … {0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]} … ) >>> libpysal.weights.util.neighbor_equality(w, wcheck) True

  inverse distance weights

      >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False)

  WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.1, 0.08944271909999159] >>> w.neighbors[0].tolist() [1, 3]

  gravity weights

      >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)

  WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.01, 0.007999999999999998]

  __init__(data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric='euclidean', radius=None)

    Casting to floats is a work around for a bug in scipy.spatial. See detail in pysal issue #126.

  Methods

  `__init__`(data, threshold[, p, alpha, ...]) | Casting to floats is a work around for a bug in scipy.spatial.
  `asymmetry`([intrinsic]) | Asymmetry check.
  `from_WSP`(WSP[, silence_warnings]) | Create a pysal W from a pysal WSP object (thin weights matrix).
  `from_adjlist`(adjlist[, focal_col, ...]) | Return an adjacency list representation of a weights object.
  `from_array`(array, threshold, **kwargs) | Construct a DistanceBand weights from an array.
  `from_dataframe`(df, threshold[, geom_col, ...]) | Make DistanceBand weights from a dataframe.
  `from_file`([path, format]) | Read a weights file into a W object.
  `from_networkx`(graph[, weight_col]) | Convert a `networkx` graph to a PySAL `W` object.
  `from_shapefile`(filepath, threshold[, idVariable]) | Distance-band based weights from shapefile
  `from_sparse`(sparse) | Convert a `scipy.sparse` array to a PySAL `W` object.
  `full`() | Generate a full `numpy.ndarray`.
  `get_transform`() | Getter for transform property.
  `plot`(gdf[, indexed_on, ax, color, node_kws, ...]) | Plot spatial weights objects.
  `remap_ids`(new_ids) | In place modification throughout `W` of id values from `w.id_order` to `new_ids` in all.
  `set_shapefile`(shapefile[, idVariable, full]) | Adding metadata for writing headers of `.gal` and `.gwt` files.
  `set_transform`([value]) | Transformations of weights.
  `symmetrize`([inplace]) | Construct a symmetric KNN weight.
  `to_WSP`() | Generate a `WSP` object.
  `to_adjlist`([remove_symmetric, drop_islands, ...]) | Compute an adjacency list representation of a weights object.
  `to_file`([path, format]) | Write a weights to a file.
  `to_networkx`() | Convert a weights object to a `networkx` graph.
  `to_sparse`([fmt]) | Generate a `scipy.sparse` array object from a pysal W.

  Attributes

  `asymmetries` | List of id pairs with asymmetric weights sorted in ascending index location order.
  `cardinalities` | Number of neighbors for each observation.
  `component_labels` | Store the graph component in which each observation falls.
  `diagW2` | Diagonal of \(WW\).
  `diagWtW` | Diagonal of \(W^{'}W\).
  `diagWtW_WW` | Diagonal of \(W^{'}W + WW\).
  `histogram` | Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.
  `id2i` | Dictionary where the key is an ID and the value is that ID's index in `W.id_order`.
  `id_order` | Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.
  `id_order_set` | Returns `True` if user has set `id_order`, `False` if not.
  `islands` | List of ids without any neighbors.
  `max_neighbors` | Largest number of neighbors.
  `mean_neighbors` | Average number of neighbors.
  `min_neighbors` | Minimum number of neighbors.
  `n` | Number of units.
  `n_components` | Store whether the adjacency matrix is fully connected.
  `neighbor_offsets` | Given the current `id_order`, `neighbor_offsets[id]` is the offsets of the id's neighbors in `id_order`.
  `nonzero` | Number of nonzero weights.
  `pct_nonzero` | Percentage of nonzero weights.
  `s0` | `s0` is defined as
  `s1` | `s1` is defined as
  `s2` | `s2` is defined as
  `s2array` | Individual elements comprising `s2`.
  `sd` | Standard deviation of number of neighbors.
  `sparse` | Sparse matrix object.
  `transform` | Getter for transform property.
  `trcW2` | Trace of \(WW\).
  `trcWtW` | Trace of \(W^{'}W\).
  `trcWtW_WW` | Trace of \(W^{'}W + WW\).

  classmethod from_array(array, threshold, **kwargs)

    Construct a DistanceBand weights from an array. Supports all the same options as `libpysal.weights.DistanceBand`

  classmethod from_dataframe(df, threshold, geom_col=None, ids=None, use_index=True, **kwargs)

    Make DistanceBand weights from a dataframe.

    Parameters:
      df `pandas.dataframe`

        a dataframe with a geometry column that can be used to construct a W object

      geom_col `python:str`

        the name of the column in df that contains the geometries. Defaults to active geometry column.

      ids list-like, `python:str`

        a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if use_index=True or a positional index is used if use_index=False. Order of the resulting W is not respected from this list.

      use_index bool

        use index of df as ids to index the spatial weights object.

  classmethod from_shapefile(filepath, threshold, idVariable=None, **kwargs)

    Distance-band based weights from shapefile

    Parameters:
      shapefile `python:str`

        shapefile name with shp suffix

      idVariable `python:str`

        name of column in shapefile’s DBF to use for ids

    Returns:
      `Kernel` `Weights` `Object`