Academic literature on the topic 'Mixed land use'

Abstract Mixed land-use is a popular concept in urban planning due to its expected role in improving environmental sustainability as well as citizen’s quality of life. Land use planning and regulations are not stringent in many cities like those in India, and policies are liberal towards mixed land uses. In these cities, mixed land-uses are a natural phenomenon manifesting under various influencing parameters. However, for studies on mixed land-uses, these cities pose data insufficiency challenges, as vital comprehensive spatial information related to land-uses is not available. Moreover, there is no standardised methodology established to assess the spatial distribution of mixed land-uses at the city level. This research has developed a GIS-based model using Weighted Overlay Analysis to predict and visualise the probability of mixed land-use at the macro or city level for the case of Pune, India. The model uses the easily available spatial data of influencing parameters of mixed land-use as input for prediction instead of comprehensive real land-use data. The model is validated by comparing the predicted mixed land-use intensities with established indicators of mixed land-use for four neighbourhoods. It is found that parameters that influence mixed land-use such as connectivity, grain pattern, population density and access to amenities can be used to predict the probability of mixed land-use. Around 35 per cent of the city area of Pune has more than 0.67 probability of mixed land-use. The model can produce the probable mixed land-use distribution across the city and can be used to compute mixed land-use intensities for neighbourhoods. Highlights for public administration, management and planning: • Mixed land-use probability distribution for Pune City, India is generated using Weighted Overlay Analysis in GIS. • As vital spatial data of land-use was unavailable, the prediction model uses data of influencing parameters of mixed land-uses such as population density, connectivity, grain pattern and access to amenities. • The mixed land-use probabilities predicted can be used to compute mixed land-use intensities of neighbourhoods. It is validated by comparing with traditional mixed land-use indicators.

This study investigates the effect of mixed land use on violence and property crime in neighborhood block groups while simultaneously considering the presence of criminogenic facilities and sociodemographic conditions. We conduct negative binomial regression to examine the relationship between mixed land use and crime and investigate whether the relationship is moderated by sociodemographic characteristics or the presence of criminogenic facilities. The results suggest that mixed land use may reduce property crime while violent crime is influenced by mixed land use in nearby neighborhoods. There was an additional effect of the presence of particular facilities, notably bars, transportation stations, schools, stores, and gas stations in the neighborhood. There was some evidence that the impact of land use mix on crime varies dependent on residential mobility, ethnic diversity and the presence of bars, transport stations, and schools. Our findings indicate that those responsible for planning urban spaces and developing land use policies should consider differential effects of land use characteristics across neighborhood contexts.

Understanding mixed-land-use practices and physicochemical influences on Escherichia (E.) coli concentrations is necessary to improve water quality management and human health. Weekly stream water samples and physicochemical data were collected from 22 stream gauging sites representing varying land use practices in a contemporary Appalachian watershed of the eastern USA. Over the period of one annual year, Escherichia (E.) coli colony forming units (CFU) per 100 mL were compared to physicochemical parameters and land use practices. Annual average E. coli concentration increased by approximately 112% from acid mine drainage (AMD) impacted headwaters to the lower reaches of the watershed (approximate averages of 177 CFU per 100 mL vs. 376 CFU per 100 mL, respectively). Significant Spearman’s correlations (p < 0.05) were identified from analyses of pH and E. coli concentration data representing 77% of sample sites; thus highlighting legacy effects of historic mining (AMD) on microbial water quality. A tipping point of 25–30% mixed development was identified as leading to significant (p < 0.05) negative correlations between chloride and E. coli concentrations. Study results advance understanding of land use and physicochemical impacts on fecal contamination in mixed-land-use watersheds, aiding in the implementation of effective water quality management practices and policies.