Hot-spots of the genus Aconitum in the Ukrainian Carpathian Region

Abstract

Basing on 2054 georeferenced occurrences of ten Aconitum species, the initial map of the species richness per each of 57 operational geographic units (OGU) of the Ukrainian Carpathian Region (UCR) has been developed. Next, to avoid unequal data distribution, we counted absolute species presence per OGU, based on which we calculated ‘rarity-weighted richness’ (RWR) and performed parsimony analysis. As a result, we found that Chornohora and Svydovets made a strongly supported cluster with the highest Aconitum species diversity and demonstrated the highest RWR values, hence being the main hot-spot of the genus distribution in the UCR. This main hot-spot also spreads to adjacent mountain ranges, including Maramures and Gorgany. Probably, a continuous gradient of Aconitum taxonomic richness from southeast toward northwest of the UCR existed before. The gap in Polonyna Borzhava, located between Polonyna Rivna and Polonyna Rivna, can be secondarily resulted by the high level of anthropogenic transformation of this region. Contrary, the high RWR value and distinct clustering of the Polonyna Rivna supported the presence of a local isolated Aconitum hot-spot related to two prominent mountain peaks located here – Runa-Plai Mt. (in Polonyna Rivna) and Pikui Mt. (in adjacen Waterdivided Mountain Range).

Analysis of the Movement Distribution Pattern of Violent Crime in Malaysia's Capital Region-Selangor, Kuala Lumpur, and Putrajaya

“…Understanding the relationship between violent crime and land use allows stakeholders to make informed decisions about land use, urban planning, and community development projects that can help reduce crime and improve overall community safety and quality of life [5][6] [7] and [8]. This allows for a more targeted approach to crime prevention and intervention efforts [9][10] [11] [12][13] [14][15] [16] and [17].…”

“…This study contributes uniquely by identifying causal links between crime rates and 13 different land uses, including (1) water bodies, (2) forests, (3) industry, (4) infrastructure & utilities, (5) institutions & community facilities, (6) open areas & recreational facilities, (7) residential & housing, (8) beaches, (9) mixed development, (10) transportation & roads, (11) trade & commerce, (12) agriculture, and (13) vacant land. This study also considers four hot spot time frames, namely midnight (12:59 a.m.), morning (7:59 a.m.), evening (12:59 p.m.), and night (7:59 p.m.), which are overlaid with land used to identify violent crime activity for each land use type.…”

“…) infrastructure & utilities,(5) institutions & community facilities,(6) open areas & recreational facilities,(7) residential & housing, (8) beaches, (9) mixed development,(10) transport & roads,(11) trade & commerce, (12) agriculture, and (13) vacant land, as potential influences on the pattern of violent crime (Figure3). Pattern Identification, Visualization, Results, Discussion and Conclusion…”