diff --git a/rstar/src/algorithm/bulk_load/bulk_load_sequential.rs b/rstar/src/algorithm/bulk_load/bulk_load_sequential.rs
index c65a3a5..ca75d8a 100644
--- a/rstar/src/algorithm/bulk_load/bulk_load_sequential.rs
+++ b/rstar/src/algorithm/bulk_load/bulk_load_sequential.rs
@@ -25,7 +25,7 @@ where
         return ParentNode::new_parent(elements);
     }
     let number_of_clusters_on_axis =
-        calculate_number_of_clusters_on_axis::<T, Params>(elements.len());
+        calculate_number_of_clusters_on_axis::<T, Params>(elements.len()).max(2);
 
     let iterator = PartitioningTask::<_, Params> {
         number_of_clusters_on_axis,
diff --git a/rstar/src/algorithm/bulk_load/cluster_group_iterator.rs b/rstar/src/algorithm/bulk_load/cluster_group_iterator.rs
index dbb6da2..6200442 100644
--- a/rstar/src/algorithm/bulk_load/cluster_group_iterator.rs
+++ b/rstar/src/algorithm/bulk_load/cluster_group_iterator.rs
@@ -55,9 +55,9 @@ where
 {
     let max_size = Params::MAX_SIZE as f32;
     // The depth of the resulting tree, assuming all leaf nodes will be filled up to MAX_SIZE
-    let depth = (number_of_elements as f32).log(max_size).ceil() as usize;
+    let depth = (number_of_elements as f32).log(max_size).ceil() as i32;
     // The number of elements each subtree will hold
-    let n_subtree = max_size.powi(depth as i32 - 1);
+    let n_subtree = max_size.powi(depth - 1);
     // How many clusters will this node contain
     let number_of_clusters = (number_of_elements as f32 / n_subtree).ceil();
 
@@ -87,7 +87,7 @@ mod test {
             assert_eq!(slab.len(), slab_size);
         }
         let mut total_size = 0;
-        let mut max_element_for_last_slab = i32::min_value();
+        let mut max_element_for_last_slab = i32::MIN;
         for slab in &slabs {
             total_size += slab.len();
             let current_max = slab.iter().max_by_key(|point| point[0]).unwrap();
diff --git a/rstar/src/algorithm/nearest_neighbor.rs b/rstar/src/algorithm/nearest_neighbor.rs
index 5b9d88f..698b26d 100644
--- a/rstar/src/algorithm/nearest_neighbor.rs
+++ b/rstar/src/algorithm/nearest_neighbor.rs
@@ -331,7 +331,7 @@ mod test {
         let sample_points = create_random_points(100, SEED_2);
         for sample_point in &sample_points {
             let mut nearest = None;
-            let mut closest_dist = ::core::f64::INFINITY;
+            let mut closest_dist = f64::INFINITY;
             for point in &points {
                 let delta = [point[0] - sample_point[0], point[1] - sample_point[1]];
                 let new_dist = delta[0] * delta[0] + delta[1] * delta[1];
diff --git a/rstar/src/algorithm/rstar.rs b/rstar/src/algorithm/rstar.rs
index 02eb197..9127037 100644
--- a/rstar/src/algorithm/rstar.rs
+++ b/rstar/src/algorithm/rstar.rs
@@ -156,13 +156,13 @@ where
     T: RTreeObject,
 {
     let all_leaves = match node.children.first() {
-        Some(RTreeNode::Leaf(_)) => return usize::max_value(),
+        Some(RTreeNode::Leaf(_)) => return usize::MAX,
         Some(RTreeNode::Parent(ref data)) => data
             .children
             .first()
             .map(RTreeNode::is_leaf)
             .unwrap_or(true),
-        _ => return usize::max_value(),
+        None => return usize::MAX,
     };
 
     let zero: <<T::Envelope as Envelope>::Point as Point>::Scalar = Zero::zero();