diff --git a/mesa_frames/abstract/agents.py b/mesa_frames/abstract/agents.py
index 93baaaa..ce412c3 100644
--- a/mesa_frames/abstract/agents.py
+++ b/mesa_frames/abstract/agents.py
@@ -735,7 +735,9 @@ def __init__(self, model: ModelDF) -> None:
 
     @abstractmethod
     def add(
-        self, agents: DataFrame | Sequence[Any] | dict[str, Any], inplace: bool = True
+        self,
+        agents: DataFrame | Sequence[Any] | dict[str, Any],
+        inplace: bool = True,
     ) -> Self:
         """Add agents to the AgentSetDF
 
diff --git a/mesa_frames/abstract/space.py b/mesa_frames/abstract/space.py
new file mode 100644
index 0000000..f20da2e
--- /dev/null
+++ b/mesa_frames/abstract/space.py
@@ -0,0 +1,323 @@
+from abc import abstractmethod
+from collections.abc import Collection, Sequence
+from typing import TYPE_CHECKING
+
+from numpy.random import Generator
+from typing_extensions import Self
+
+from mesa_frames.abstract.agents import AgentContainer
+from mesa_frames.abstract.mixin import CopyMixin, DataFrameMixin
+from mesa_frames.types_ import (
+    DataFrame,
+    GeoDataFrame,
+    IdsLike,
+    SpaceCoordinate,
+    SpaceCoordinates,
+)
+
+ESPG = int
+
+if TYPE_CHECKING:
+    from mesa_frames.concrete.model import ModelDF
+
+
+class SpaceDF(CopyMixin, DataFrameMixin):
+    _model: "ModelDF"
+    _agents: DataFrame | GeoDataFrame
+
+    def __init__(self, model: "ModelDF") -> None:
+        """Create a new SpaceDF object.
+
+        Parameters
+        ----------
+        model : 'ModelDF'
+
+        Returns
+        -------
+        None
+        """
+        self._model = model
+
+    def random_agents(
+        self,
+        n: int,
+        seed: int | None = None,
+    ) -> DataFrame:
+        """Return a random sample of agents from the space.
+
+        Parameters
+        ----------
+        n : int
+            The number of agents to sample
+        seed : int | None, optional
+            The seed for the sampling, by default None
+            If None, an integer from the model's random number generator is used.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the sampled agents
+        """
+        if seed is None:
+            seed = self.random.integers(0)
+        return self._df_sample(self._agents, n=n, seed=seed)
+
+    @abstractmethod
+    def get_directions(
+        self,
+        pos0: SpaceCoordinate | SpaceCoordinates | None = None,
+        pos1: SpaceCoordinate | SpaceCoordinates | None = None,
+        agents0: IdsLike | AgentContainer | Collection[AgentContainer] | None = None,
+        agents1: IdsLike | AgentContainer | Collection[AgentContainer] | None = None,
+        normalize: bool = False,
+    ) -> DataFrame:
+        """Returns the directions from pos0 to pos1 or agents0 and agents1.
+        If the space is a Network, the direction is the shortest path between the two nodes.
+        In all other cases, the direction is the direction vector between the two positions.
+        Either positions (pos0, pos1) or agents (agents0, agents1) must be specified, not both and they must have the same length.
+
+        Parameters
+        ----------
+        pos0 : SpaceCoordinate | SpaceCoordinates | None, optional
+            The starting positions
+        pos1 : SpaceCoordinate | SpaceCoordinates | None, optional
+            The ending positions
+        agents0 : IdsLike | AgentContainer | Collection[AgentContainer] | None, optional
+            The starting agents
+        agents1 : IdsLike | AgentContainer | Collection[AgentContainer] | None, optional
+            The ending agents
+        normalize : bool, optional
+            Whether to normalize the vectors to unit norm. By default False
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame where each row represents the direction from pos0 to pos1 or agents0 to agents1
+        """
+        ...
+
+    @abstractmethod
+    def get_distances(
+        self,
+        pos0: SpaceCoordinate | SpaceCoordinates | None = None,
+        pos1: SpaceCoordinate | SpaceCoordinates | None = None,
+        agents0: IdsLike | AgentContainer | Collection[AgentContainer] | None = None,
+        agents1: IdsLike | AgentContainer | Collection[AgentContainer] | None = None,
+    ) -> DataFrame:
+        """Returns the distances from pos0 to pos1 or agents0 and agents1.
+        If the space is a Network, the distance is the number of nodes of the shortest path between the two nodes.
+        In all other cases, the distance is Euclidean/l2/Frobenius norm.
+        You should specify either positions (pos0, pos1) or agents (agents0, agents1), not both and they must have the same length.
+
+        Parameters
+        ----------
+        pos0 : SpaceCoordinate | SpaceCoordinates | None, optional
+            The starting positions
+        pos1 : SpaceCoordinate | SpaceCoordinates | None, optional
+            The ending positions
+        agents0 : IdsLike | AgentContainer | Collection[AgentContainer], optional
+            The starting agents
+        agents1 : IdsLike | AgentContainer | Collection[AgentContainer], optional
+            The ending agents
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame where each row represents the distance from pos0 to pos1 or agents0 to agents1
+        """
+        ...
+
+    @abstractmethod
+    def get_neighbors(
+        self,
+        radius: int | float | Sequence[int] | Sequence[float],
+        pos: SpaceCoordinate | SpaceCoordinates | None = None,
+        agents: IdsLike | AgentContainer | Collection[AgentContainer] | None = None,
+        include_center: bool = False,
+    ) -> DataFrame:
+        """Get the neighboring agents from given positions or agents according to the specified radiuses.
+        Either positions (pos0, pos1) or agents (agents0, agents1) must be specified, not both and they must have the same length.
+
+        Parameters
+        ----------
+        radius : int | float | Sequence[int] | Sequence[float]
+            The radius(es) of the neighborhood
+        pos : SpaceCoordinate | SpaceCoordinates | None, optional
+            The coordinates of the cell to get the neighborhood from, by default None
+        agents : IdsLike | AgentContainer | Collection[AgentContainer] | None, optional
+            The id of the agents to get the neighborhood from, by default None
+        include_center : bool, optional
+            If the center cells or agents should be included in the result, by default False
+
+        Returns
+        -------
+        DataFrame
+            A dataframe with neighboring agents.
+            The columns with '_center' suffix represent the center agent/position.
+
+        Raises
+        ------
+        ValueError
+            If both pos and agent are None or if both pos and agent are not None.
+        """
+        ...
+
+    @abstractmethod
+    def move_agents(
+        self,
+        agents: IdsLike | AgentContainer | Collection[AgentContainer],
+        pos: SpaceCoordinate | SpaceCoordinates,
+        inplace: bool = True,
+    ) -> Self:
+        """Place agents in the space according to the specified coordinates. If some agents are already placed,
+        raises a RuntimeWarning.
+
+        Parameters
+        ----------
+        agents : IdsLike | AgentContainer | Collection[AgentContainer]
+            The agents to place in the space
+        pos : SpaceCoordinate | SpaceCoordinates
+            The coordinates for each agents. The length of the coordinates must match the number of agents.
+        inplace : bool, optional
+            Whether to perform the operation inplace, by default True
+
+        Raises
+        ------
+        RuntimeWarning
+            If some agents are already placed in the space.
+        ValueError
+            - If some agents are not part of the model.
+            - If agents is IdsLike and some agents are present multiple times.
+
+        Returns
+        -------
+        Self
+        """
+        ...
+
+    @abstractmethod
+    def move_to_empty(
+        self,
+        agents: IdsLike | AgentContainer | Collection[AgentContainer],
+        inplace: bool = True,
+    ) -> Self:
+        """Move agents to empty cells/positions in the space (cells/positions where there isn't any single agent).
+
+        Parameters
+        ----------
+        agents : IdsLike | AgentContainer | Collection[AgentContainer]
+            The agents to move to empty cells/positions
+        inplace : bool, optional
+            Whether to perform the operation inplace, by default True
+
+        Returns
+        -------
+        Self
+        """
+        ...
+
+    @abstractmethod
+    def random_pos(
+        self,
+        n: int,
+        seed: int | None = None,
+    ) -> DataFrame:
+        """Return a random sample of positions from the space.
+
+        Parameters
+        ----------
+        n : int
+            The number of positions to sample
+        seed : int | None, optional
+            The seed for the sampling, by default None
+            If None, an integer from the model's random number generator is used.
+
+        Returns
+        -------
+        DataFrame
+            A DataFrame with the sampled positions
+        """
+        ...
+
+    @abstractmethod
+    def remove_agents(
+        self,
+        agents: IdsLike | AgentContainer | Collection[AgentContainer],
+        inplace: bool = True,
+    ):
+        """Remove agents from the space.
+
+        Parameters
+        ----------
+        agents : IdsLike | AgentContainer | Collection[AgentContainer]
+            The agents to remove from the space
+        inplace : bool, optional
+            Whether to perform the operation inplace, by default True
+
+        Raises
+        ------
+        ValueError
+            If some agents are not part of the model.
+
+        Returns
+        -------
+        Self
+        """
+        ...
+
+    @abstractmethod
+    def swap_agents(
+        self,
+        agents0: IdsLike | AgentContainer | Collection[AgentContainer],
+        agents1: IdsLike | AgentContainer | Collection[AgentContainer],
+    ) -> Self:
+        """Swap the positions of the agents in the space.
+        agents0 and agents1 must have the same length and all agents must be placed in the space.
+
+        Parameters
+        ----------
+        agents0 : IdsLike | AgentContainer | Collection[AgentContainer]
+            The first set of agents to swap
+        agents1 : IdsLike | AgentContainer | Collection[AgentContainer]
+            The second set of agents to swap
+
+        Returns
+        -------
+        Self
+        """
+
+    @abstractmethod
+    def __repr__(self) -> str: ...
+
+    @abstractmethod
+    def __str__(self) -> str: ...
+
+    @property
+    def agents(self) -> DataFrame | GeoDataFrame:
+        """Get the ids of the agents placed in the cell set, along with their coordinates or geometries
+
+        Returns
+        -------
+        AgentsDF
+        """
+        return self._agents
+
+    @property
+    def model(self) -> "ModelDF":
+        """The model to which the space belongs.
+
+        Returns
+        -------
+        'ModelDF'
+        """
+        return self._model
+
+    @property
+    def random(self) -> Generator:
+        """The model's random number generator.
+
+        Returns
+        -------
+        Generator
+        """
+        return self.model.random
diff --git a/mesa_frames/concrete/model.py b/mesa_frames/concrete/model.py
index be4ef43..2c58125 100644
--- a/mesa_frames/concrete/model.py
+++ b/mesa_frames/concrete/model.py
@@ -4,6 +4,7 @@
 import numpy as np
 from typing_extensions import Any
 
+from mesa_frames.abstract.space import SpaceDF
 from mesa_frames.concrete.agents import AgentsDF
 
 if TYPE_CHECKING:
@@ -59,6 +60,7 @@ class ModelDF:
     _seed: int | Sequence[int]
     running: bool
     _agents: AgentsDF
+    _space: SpaceDF | None  # This will be a MultiSpaceDF object
 
     def __new__(
         cls, seed: int | Sequence[int] | None = None, *args: Any, **kwargs: Any
@@ -77,6 +79,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         self.schedule = None
         self.current_id = 0
         self._agents = AgentsDF(self)
+        self._space = None
 
     def get_agents_of_type(self, agent_type: type) -> "AgentSetDF":
         """Retrieve the AgentSetDF of a specified type.
@@ -147,3 +150,15 @@ def agents(self, agents: AgentsDF) -> None:
     @property
     def agent_types(self) -> list[type]:
         return [agent.__class__ for agent in self._agents._agentsets]
+
+    @property
+    def space(self) -> SpaceDF:
+        if not self._space:
+            raise ValueError(
+                "You haven't set the space for the model. Use model.space = your_space"
+            )
+        return self._space
+
+    @space.setter
+    def space(self, space: SpaceDF) -> None:
+        self._space = space
diff --git a/mesa_frames/types_.py b/mesa_frames/types_.py
index c34e792..b1b4ddf 100644
--- a/mesa_frames/types_.py
+++ b/mesa_frames/types_.py
@@ -1,6 +1,10 @@
 from collections.abc import Collection
 from typing import Literal
 
+from collections.abc import Sequence
+
+import geopandas as gpd
+import geopolars as gpl
 import pandas as pd
 import polars as pl
 from numpy import ndarray
@@ -15,20 +19,55 @@
 AnyArrayLike = ArrayLike | pd.Index | pd.Series
 PandasMaskLike = AgnosticMask | pd.Series | pd.DataFrame | AnyArrayLike
 PandasIdsLike = AgnosticIds | pd.Series | pd.Index
+PandasGridCapacity = ndarray
 
 ###----- Polars Types -----###
 
 PolarsMaskLike = AgnosticMask | pl.Expr | pl.Series | pl.DataFrame | Collection[int]
 PolarsIdsLike = AgnosticIds | pl.Series
+PolarsGridCapacity = list[pl.Expr]
 
 ###----- Generic -----###
-
+GeoDataFrame = gpd.GeoDataFrame | gpl.GeoDataFrame
 DataFrame = pd.DataFrame | pl.DataFrame
 Series = pd.Series | pl.Series
+Series = pd.Series | pl.Series
 Index = pd.Index | pl.Series
 BoolSeries = pd.Series | pl.Series
 MaskLike = AgnosticMask | PandasMaskLike | PolarsMaskLike
 IdsLike = AgnosticIds | PandasIdsLike | PolarsIdsLike
 
+
 ###----- Time ------###
 TimeT = float | int
+
+###----- Space -----###
+
+NetworkCoordinate = int | DataFrame
+
+GridCoordinate = int | Sequence[int] | DataFrame
+
+DiscreteCoordinate = NetworkCoordinate | GridCoordinate
+ContinousCoordinate = float | Sequence[float] | DataFrame
+
+SpaceCoordinate = DiscreteCoordinate | ContinousCoordinate
+
+
+NetworkCoordinates = NetworkCoordinate | Collection[NetworkCoordinate]
+GridCoordinates = (
+    GridCoordinate | Sequence[int | slice | Sequence[int]] | Collection[GridCoordinate]
+)
+
+DiscreteCoordinates = NetworkCoordinates | GridCoordinates
+ContinousCoordinates = (
+    ContinousCoordinate
+    | Sequence[float | Sequence[float]]
+    | Collection[ContinousCoordinate]
+)
+
+SpaceCoordinates = DiscreteCoordinates | ContinousCoordinates
+
+GridCapacity = PandasGridCapacity | PolarsGridCapacity
+NetworkCapacity = DataFrame
+
+DiscreteSpaceCapacity = GridCapacity | NetworkCapacity
diff --git a/pyproject.toml b/pyproject.toml
index 137e98e..f7091cd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,10 +19,13 @@ dependencies = [
 pandas = [
   "pandas~=2.2",
   "pyarrow",
+  "geopandas"
 ]
 polars = [
-  "polars>=1.0.0", #polars._typing (see mesa_frames.types_) added in 1.0.0
+  "polars>=1.0.0", #polars._typing (see mesa_frames.types) added in 1.0.0
+  "geopolars"
 ]
+
 dev = [
   "mesa_frames[pandas,polars]",
   "perfplot", #readme_script